Machine based three-dimensional (3d) object defect detection

ABSTRACT

Implementations describe systems and methods for machine based defect detection of three-dimensional (3D) printed objects. A method of one embodiment of the disclosure includes providing a first illumination of a 3D printed object using a first light source arrangement. A plurality of images of the 3D printed object are then generated using one or more imaging devices. Each image may depict a distinct region of the 3D printed object. The plurality of images may then be processed by a processing device using a machine learning model trained to identify one or more types of manufacturing defects of a 3D printing process. The machine learning model may provide a probability that an image contains a manufacturing defect. The processing device may then determine, without user input, whether the 3D printed object contains one or more manufacturing defects based on the results provided by the machine learning model.

RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication No. 62/768,770, filed Nov. 16, 2018, entitled “MACHINE BASEDTHREE-DIMENSIONAL (3D) PRINTED OBJECT DEFECT DETECTION,” which isincorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates to the field of manufacturing customproducts and, in particular, to detecting defects within the internalvolume, on the surface, and on the interface of customized,three-dimensional (3D) printed products, as well as detecting defects onshells formed over 3D printed objects.

BACKGROUND

For some applications, customized products may be fabricated usingthree-dimensional (3D) printing systems and methods. 3D printed productsmay contain various defects resulting from imperfections of thefabrication machine or the surrounding environment. Defects may becaused, for example, by vibrations, air bubbles trapped in resin, laserpower fluctuations and/or other machine dependent factors.

Defects within the internal volume of a 3D printed object, defects on asurface of a 3D printed object and defects at a surface to interiorvolume interface of a 3D printed object can all render 3D printedobjects unsuitable for their intended purpose. The standard technique todetect such defects is through manual inspection or examination by aperson. However, this examination procedure is highly subjective andfrequently results in a high number of both false positivedeterminations of defects, and false negative determinations of defects.As a result, 3D printed objects without significant defects are eitherthrown out or repurposed, and 3D printed objects with significantdefects are overlooked, which may cause the 3D printed object tomalfunction or break during use.

Additionally, 3D printed molds may be used to form shells such asorthodontic aligners. Such shells may be formed (e.g., thermoformed)over the 3D printed molds. Such shells are also subject to defects,which may include defects transferred from the 3D printed molds.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that differentreferences to “an” or “one” embodiment in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone.

FIG. 1A illustrates one embodiment of an imaging defect detection systemthat performs automated defect detection of three-dimensional (3D)printed objects and/or shells, in accordance with one embodiment.

FIG. 1B illustrates a 3D view of one embodiment of an imaging systemthat captures images of 3D printed objects and/or shells, in accordancewith one embodiment.

FIG. 1C illustrates a dot pattern around a 3D object, in accordance withan embodiment.

FIG. 2A illustrates one embodiment of an imaging defect detection systemthat performs automated defect detection of 3D printed objects and/orshells, in accordance with one embodiment.

FIG. 2B illustrates one embodiment of imaging defect detection systemthat performs automated defect detection of 3D printed objects and/orshells, in accordance with one embodiment.

FIG. 3A illustrates a flow diagram for a method of detectingmanufacturing defect in a 3D printed object, in accordance with oneembodiment.

FIG. 3B illustrates a flow diagram for a method of determining anidentifier (ID) associated with a 3D printed object, in accordance withone embodiment.

FIG. 4 illustrates a flow diagram for a method of determining a firstillumination for capturing an image of a 3D printed object, inaccordance with one embodiment.

FIG. 5 illustrates a flow diagram for a method of detecting a grossdefect of a 3D printed object, in accordance with one embodiment.

FIG. 6 illustrates a flow diagram for a method of processing an imagecaptured by an imaging system to detect a layering defect in a 3Dprinted object, in accordance with one embodiment.

FIG. 7 illustrates a 3D printed object, including layers resulting froma 3D printing process, in accordance with one embodiment.

FIG. 8A illustrates an image of a 3D printed object generated by animaging system depicted in FIGS. 1A, 1B, 2A and/or 2B, in accordancewith one embodiment.

FIG. 8B illustrates an exploded view of a distinct region of the 3Dprinted object illustrated in FIG. 8A, wherein the 3D printed objectdoes not contain a layering defect, in accordance with one embodiment.

FIG. 8C illustrates an exploded view of a distinct region of the 3Dprinted object illustrated in FIG. 8A, wherein the 3D printed objectcontains a layering defect, in accordance with one embodiment.

FIG. 9 illustrates an example user interface (UI) for system control andimage acquisition functions of an imager control module, in accordancewith one embodiment.

FIG. 10 illustrates an example UI for engineering control functions ofan imager control module, in accordance with one embodiment.

FIG. 11 illustrates a block diagram of an example computing device, inaccordance with embodiments.

FIG. 12A illustrates a tooth repositioning appliance, in accordance withembodiments.

FIG. 12B illustrates a tooth repositioning system, in accordance withembodiments.

FIG. 13 illustrates a method of orthodontic treatment using a pluralityof appliances, in accordance with embodiments.

FIG. 14 illustrates a method for designing an orthodontic appliance tobe produced by direct fabrication, in accordance with embodiments.

FIG. 15 illustrates a method for digitally planning an orthodontictreatment, in accordance with embodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

Described herein are embodiments covering systems, methods, and/orcomputer-readable media suitable for machine based defect detection ofdefects in three-dimensional (3D) printed objects. The 3D printedobjects may be any type of 3D printed object, one example of which arecustomized medical devices. For example, in some embodiments, machinebased defect detection systems and methods may be implemented in theinspection of molds for orthodontic or polymeric aligners prior to themanufacturing of orthodontic or polymeric aligners. In otherembodiments, machine based defect detection systems and methods may beimplemented in the inspection of orthodontic or polymeric alignersmanufactured by direct fabrication. In other embodiments, machine baseddefect detection systems and methods may be implanted in the inspectionof shells formed over 3D printed objects (e.g., over 3D printed molds).For example, the 3D printed objects may be 3D printed molds of dentalarches, and shells such as orthodontic aligners or other orthodonticappliances may be formed over the 3D printed molds. The shells may beremoved from the 3D printed molds, and then processed by the machinebased defect detection systems described herein to determine defects inthe shells.

In some embodiments, a 3D printed object may be fabricated usingadditive manufacturing techniques (also referred to herein as “3Dprinting”). To manufacture the 3D printed object, a shape of the objectmay be determined and designed using computer aided engineering (CAE) orcomputer aided design (CAD) programs. In some instances,stereolithography (SLA), also known as optical fabrication solidimaging, may be used to fabricate the 3D printed object. In SLA, theobject is fabricated by successively printing thin layers of aphoto-curable material (e.g., a polymeric resin) on top of one another.A platform rests in a bath of liquid photopolymer or resin just below asurface of the bath. A light source (e.g., an ultraviolet laser) tracesa pattern over the platform, curing the photopolymer where the lightsource is directed, to form a first layer of the object. The platform islowered incrementally, and the light source traces a new pattern overthe platform to form another layer of the object at each increment. Thisprocess repeats until the object is completely fabricated. Once all ofthe layers of the object are formed, the object may be cleaned andcured.

In some embodiments, 3D printed objects may be produced using otheradditive manufacturing techniques. Other additive manufacturingtechniques may include: (1) material jetting, in which material isjetted onto a build platform using either a continuous or drop on demand(DOD) approach; (2) binder jetting, in which alternating layers of abuild material (e.g., a powder-based material) and a binding material(e.g., a liquid binder) are deposited by a print head; (3) fuseddeposition modeling (FDM), in which material is drawn through a nozzle,heated, and deposited layer by layer; (4) powder bed infusion, includingbut not limited to direct metal laser sintering (DMLS), electron beammelting (EBM), selective heat sintering (SHS), selective laser melting(SLM), and selective laser sintering (SLS); (5) sheet lamination,including but not limited to laminated object manufacturing (LOM) andultrasonic additive manufacturing (UAM); and (6) directed energydeposition, including but not limited to laser engineering net shaping,directed light fabrication, direct metal deposition, and 3D lasercladding.

In some embodiments, a 3D printed object may include a mold for anorthodontic and/or polymeric aligner. The mold may have the shape of apatient's dental arch and an aligner may be formed over the mold. Tomanufacture the mold, a shape of the dental arch for the patient at atreatment stage may be determined based on a custom treatment plan. Inthe example of orthodontics, the treatment plan may be generated basedon an intraoral scan of a dental arch to be modeled. The intraoral scanmay be performed to generate a 3D virtual model of the patient's dentalarch. In some instances, SLA techniques may be used to fabricate themold of the patient's dental arch in accordance with the descriptionabove.

An aligner may be formed from each mold of the patient's dental arch. Inone embodiment, a sheet of material is pressure formed or thermoformedover the mold. To thermoform the aligner over the mold, the sheet ofmaterial may be heated to a temperature at which the sheet becomespliable. Pressure may concurrently be applied to the sheet to form thenow pliable sheet around the mold. In some embodiments, vacuum isapplied to remove trapped air and pull the sheet onto the mold alongwith pressurized air to form the sheet to the detailed shape of themold. Once the sheet cools, it will have a shape that conforms to themold.

If the mold contains defects within its internal volume, on its surface,or on its interface, those defects may be transferred to the laterformed aligner. For example, a gap may exist between one or more thinlayers of the mold as a result of a malfunction of the moldmanufacturing process, causing air to become trapped within that gap.When vacuum is applied to remove trapped air during aligner manufacture,the air trapped in the gap between the thin layers of the mold may beremoved and the thin layers may be forced together, closing the gap whenpressure is applied to the plastic sheet. This type of defect isreferred to herein as an “internal volume defect.” Internal volumedefects may cause a deformation of the mold of the patient's dental archduring thermoforming of the aligner, which may be transferred to thealigner formed over the deformed mold. In another example, particles(e.g., debris), may form or collect on the surface of the mold. Theshape of the particles may transfer to the aligner during thethermoforming process. This type of defect is referred to herein as a“surface defect.” In a further example, holes (e.g., pits) may form atthe interface of the internal volume and the surface of the mold. Theshape of the holes may transfer to the aligner during the thermoformingprocess. This type of defect is referred to herein as an “interfacedefect.”

Internal volume defects, surface defects, interface defects, and otherdefects caused during fabrication of 3D printed objects may be referredto herein as “layering defects.” Layering defects may also includeprinted layers with abnormal layer thickness (e.g., layers with athickness that exceeds a layer thickness threshold) and delaminationbetween printed layers. In some embodiments, a layering defecttransferred to an aligner may cause a patient using the aligner toexperience discomfort. In further embodiments, a layering defecttransferred to the aligner may cause an aligner to fail to impartplanned forces on a patient's teeth. In further embodiments, a layeringdefect transferred to an aligner may cause the aligner to fail toproperly fit a patient's dental arch. It should be noted that, whilelayering defects may be present in a mold of a dental arch used to forman orthodontic or polymeric aligner, these defects may also be presentin any type of 3D printed object, in accordance with any fabricationtechnique described herein and/or any other 3D printing technique.

In other embodiments, an orthodontic and/or polymeric aligner may bemanufactured via direct fabrication. Direct fabrication may includeadditive manufacturing techniques described above, or subtractivemanufacturing techniques (e.g., milling). In some embodiments, directfabrication involves forming an object (e.g., an orthodontic orpolymeric aligner or a portion thereof) without using a physicaltemplate (e.g., mold, mask, etc.). In some embodiments, directfabrication methods build up the object geometry in a layer-by-layerfashion, with successive layers being formed in discrete build steps.Alternatively or in combination, direct fabrication methods that allowfor continuous buildup of an object geometry can be used. Other directfabrication methods allowing for the buildup of object geometry in alayer-by-layer fashion may also be used.

Layering defects (e.g., internal voids, pits at a surface to interiorvolume interface, surface debris, layers with a thickness that deviatesfrom a target layer thickness, delamination of layers, etc.) may form inan aligner formed by direct fabrication techniques. In some embodiments,a layering defect on the aligner may result in a patient using thealigner to experience discomfort, may cause the aligner to fail toimpart planned forces on a patient's teeth, and/or may cause the alignerto fail to properly fit a patient's dental arch.

As mentioned above, some embodiments of the present disclosure maydetect various manufacturing defects (e.g., layering defects) in 3Dprinted objects (e.g., for a given set of molds, or a given set ofaligners). The manufacturing defects may include gross defects, as wellas layering defects. Gross defects may include one or more of archvariation, deformation, bend (compressed or expanded) of molds oraligners, outline variations, webbing, trimmed attachments, missingattachments, burrs, flaring, power ridge issues, material breakage,short hooks, and so forth. Detection of the manufacturing defects mayenable fixing the mold or aligner to remove the defect, preventing theshipment of a deformed or subpar aligner, and/or remanufacture of thedeformed or subpar mold or aligner prior to shipment.

It should be noted that “aligner”, “appliance” and “shell” may be usedinterchangeably herein. Some embodiments are discussed herein withreference to molds for orthodontic and/or polymeric aligners and toorthodontic and/or polymeric aligners (also referred to simply asaligners). However, embodiments also extend to other types of molds,such as molds for orthodontic retainers, orthodontic splints, sleepappliances for mouth insertion (e.g., for minimizing snoring, sleepapnea, etc.) and/or molds for non-dental applications. Embodiments mayalso extend to other types of shells formed over molds, such asorthodontic retainers, orthodontic splints, sleep appliances for mouthinsertion, etc. Other applications of the present disclosure may befound when inspecting 3D printed palatal expanders, removable mandibularrepositioning devices, and removable surgical fixation devices.Additionally, embodiments also extend to any type of 3D printed objects.Accordingly, it should be understood that embodiments herein that referto molds and aligners also apply to other types of dental appliances.Additionally, the principles, features and methods discussed may beapplied to any application or process in which it is useful to performmachine based defect detection for any suitable type of 3D printedobjects (e.g., customized devices, such as eye glass frames, contact orglass lenses, hearing aids or plugs, artificial knee caps, prostheticlimbs and devices, orthopedic inserts, as well as protective equipmentsuch as knee guards, athletic cups, or elbow, chin, and shin guards andother athletic/protective devices).

Furthermore, while embodiments are discussed herein with reference toperforming automated image quality control of 3D printed objects.Embodiments also apply to performing automated image quality control ofshells formed over 3D printed objects (e.g., to polymeric alignersthermoformed over 3D printed molds of dental arches).

In one embodiment, a method of performing automated quality control of a3D printed object includes providing a first illumination of the 3Dprinted object using a first light source arrangement. A plurality ofimages of the 3D printed object are generated under the firstillumination using one or more imaging devices, wherein each image ofthe plurality of images depicts a distinct region of the 3D printedobject. The plurality of images are processed by a processing deviceusing a machine learning model trained to identify one or more types ofmanufacturing defects of a 3D printing process, wherein an output of themachine learning model comprises, for each type of manufacturing defect,a probability that an image comprises a defect of that type ofmanufacturing defect. The processing device then determines, withoutuser input, whether the 3D printed object comprises one or moremanufacturing defects based on a result of the processing.

In one embodiment, a method of performing automated quality control of a3D printed object includes obtaining, by a processing device, an imageof a three-dimensional (3D) printed object. The processing deviceperforms edge detection on the image to determine a boundary of the 3Dprinted object in the image. The processing device selects a set ofpoints on the boundary. The processing device determines an area ofinterest using the set of points, wherein the area of interest comprisesa first region of the image that depicts the 3D printed object withinthe boundary. The processing device crops the image to exclude a secondregion of the image that is outside of the area of interest. Theprocessing device processes the cropped image using a machine learningmodel trained to identify manufacturing defects of a 3D printingprocess, wherein an output of the machine learning model comprises aprobability that the 3D printed object in the image comprises amanufacturing defect. The processing device then determines whether the3D printed object depicted in the image comprises a defect from theoutput.

In one embodiment, a defect detection system of three-dimensional (3D)printed objects includes a multi-axis platform to support a 3D printedobject, a plurality of light sources to illuminate the 3D printedobject, a computing device, and one or more imaging devices to generatea plurality of images of the 3D printed object from a plurality ofrotation and/or translational motion settings of the multi-axisplatform, wherein each image of the plurality of images depicts adistinct region of the 3D printed object. The computing device processesthe first plurality of images using a machine learning model trained toidentify manufacturing defects of a 3D printing process, wherein anoutput of the machine learning model comprises a probability that animage comprises a manufacturing defect. The computing device furtherdetermines, without user input, whether the 3D printed object comprisesone or more manufacturing defects based on a result of the output of themachine learning model.

Traditionally, 3D printed parts are inspected manually for qualitycontrol. Such manual inspection is highly subjective and frequentlyresults in a high number of both false positive determinations ofdefects, and false negative determinations of defects. Additionally,there is often little consistency between inspectors. As a result, 3Dprinted objects without significant defects are either thrown out orrepurposed, and 3D printed objects with significant defects areoverlooked. The automated image-based quality control system andtechniques described in embodiments are more accurate and more uniformthan traditional manual inspection of 3D printed parts. Accordingly,false positive determinations of defects and false negativedeterminations of defects may be reduced or eliminated in embodiments.Additionally, the consistency of quality in 3D printed parts may beincreased.

Various software and/or hardware components may be used to implement thedisclosed embodiments, as shown in FIGS. 1A, 1B, 2A, 2B and 11. Forexample, software components may include computer instructions stored ina tangible, non-transitory computer-readable media that are executed byone or more processing devices to perform machine based defect detectionof 3D printed objects, such as aligners or molds for aligners. Thesoftware may setup and calibrate cameras included in the hardwarecomponents, capture images of 3D printed objects from various anglesusing the cameras, setup and calibrate light source arrangement includedin the hardware components, provide illumination arrangements toproperly illuminate the 3D printed object while images are captured,perform analysis that compares a digital model of the 3D printed objectwith the image of the 3D printed object to detect one or more grossdefects (e.g., deformation, outline variation, etc.), and/or performautomated analysis that identifies fine defects such as layering defectsin the 3D printed object using a trained machine learning model.

Referring now to the figures, FIG. 1A illustrates one embodiment of adefect detection system 100 that performs automated defect detection ofa 3D object 114, in accordance with one embodiment. In one embodiment,the 3D object 114 is a 3D printed object. In one embodiment, the 3Dobject 114 is a shell that was formed over a 3D printed object, and thenoptionally removed therefrom. The defect detection system 100 mayinclude an imaging system 101 and a computing device 135. The imagingsystem 101 may include a platform apparatus 102, a top view cameraapparatus 116, and/or a side view camera apparatus 124. The platformapparatus 102, top view camera apparatus 116 and/or side view cameraapparatus 124 may be connected to computing device 135 via a wired orwireless connection. The computing device 135 may include an imagercontrol module 140, which may send instructions to the platformapparatus 102, top view camera apparatus 116 and/or side view cameraapparatus 124 to cause the defect detection system 100 to capture imagesof one or more regions of a 3D object 114 disposed on the platformapparatus 102. The captured images may be sent to the computing device135, and an image inspection module 145 on the computing device 135 mayanalyze the images of the 3D object 114 to determine whether anymanufacturing defects (e.g., gross defects, layering defects, etc.) arepresent in the 3D object 114.

The platform apparatus 102 may include a platform 104. The 3D object 114may sit on the platform 104 while images of the 3D printed object arecaptured and subsequently processed by a processing logic. In oneembodiment, the platform 104 may be a multi-axis platform. In oneembodiment, the multi-axis platform includes an x-y-z-θ control,allowing the platform 104 to move along 4 axes of motion. Alternatively,the multi-axis platform may include fewer degrees of control (e.g., a θcontrol that causes the multi-axis platform to rotate around a z-axis).The 3D object 114 may be secured in a stationary position by a partholder (not shown) in some embodiments. Alternatively, the 3D object 114may rest on the platform 104 without use of a part holder. Imagercontrol module 140 may send instructions to platform apparatus 102 toset a motion setting of the platform 104 and cause the platform 104 (andthe 3D printed object disposed thereon) to move along or around at leastone axis of motion (e.g., rotation and/or translational motion in the x,y, and/or z axes). In some embodiments, the platform 104 is rotatedcontinuously while images are generated. Alternatively, the platform 104may be rotated to a target orientation, and then rotation may be stoppedwhile one or more images are generated.

The platform apparatus 102 may further include one or more lightsources. The light sources may include a first light source 106 disposedbeneath the platform 104, which may include a first set of one or morelight emitting elements 108. Each light emitting element 108 may includeat least one of an incandescent light bulb, a fluorescent light bulb, alight-emitting diode (LED), a neon lamp, and so forth. In oneembodiment, the one or more of the light emitting elements 108 may emitfull spectrum light. In one embodiment, one or more of the lightemitting elements 108 may emit light of a particular wavelength orspectrum. For example, light emitting elements 108 may emit, red light,blue light, green light, infrared light, ultraviolet light, and so on.First light source 106 may include light emitting elements 108 that emitvarious different wavelengths or spectrums of light in embodiments. Forexample, some light emitting elements 108 may emit infrared light, whileother light emitting elements may emit full spectrum light. In oneembodiment, the platform 104 maybe composed of a transparent material,allowing illumination from the first light source 106 below the platformto pass through the platform 104 and provide illumination of a bottom ofthe 3D object 114 from underneath the 3D object 114.

The platform apparatus 102 may further include a backing plate 110. The3D object 114 may be disposed between the side view camera apparatus 124and the backing plate 110. The backing plate 110 may facilitate imagesof the 3D object 114 with adequate contrast and/or lighting conditions.The backing plate 110 may include a second light source 112, wherein thesecond light source 112 may include a second set of one or more lightemitting elements 108. The second light source 112 may provideillumination to at least one side of the 3D object 114. Second lightsource 112 may include light emitting elements 108 that emit variousdifferent wavelengths or spectrums of light in embodiments. For example,some light emitting elements 108 may emit infrared light, while otherlight emitting elements may emit full spectrum light. In one embodiment,backing plate 110 has a curved shape with a concave face that faces theplatform 104 and 3D object 114 disposed thereon.

A third light source 120 may be disposed over the platform 104, and mayprovide illumination on a top of the 3D object 114, The third lightsource 120 may include a third set of one or more light emittingelements 108. Third light source 120 may include light emitting elements108 that emit various different wavelengths or spectrums of light inembodiments. For example, some light emitting elements 108 may emitinfrared light, while other light emitting elements may emit fullspectrum light. In one embodiment, third light source 120 is a componentof top view camera apparatus 116. Alternatively, third light source 120may be a separate component, which may be connected to computing device135.

In one embodiment, one or more of the first light source 106, secondlight source 112 and/or third light source 120 are components of a smartlighting system. The smart lighting system may be controlled by imagercontrol module 140, which may determine a target illumination for the 3Dobject 114 and activate one or more light emitting elements 108 of thefirst light source 106, second light source 112 and/or third lightsource 120 to achieve the target illumination. Additionally, the smartlighting system may adjust an intensity of one or more of the lightemitting elements. The light emitting elements 108 in the first lightsource 106, second light source 112 and/or third light source 120 may bearranged in a pattern (e.g., as a grid of light emitting elements), andeach light emitting element may provide illumination of the 3D printedobject from a particular angle. Additionally, or alternatively, one ormore of the light emitting elements 108 in the first light source 106,second light source 112 and/or third light source 120 may be moveable,and may be positioned to achieve light from a target angle. The imagercontrol module 140 may determine a target illumination based on a sizeof the 3D printed object, shapes of the 3D object 114, a material thatthe 3D printed object is composed of, and so on. The target illuminationmay cause a target region of the 3D object 114 that will be imaged to beilluminated, with minimal or no shadows occluding features of the 3Dobject 114. The imager control module 140 may also cause the first lightsource 106, second light source 112 and/or third light source 120 tocycle through different illumination settings until an adequate oroptimal illumination setting is identified for a particular 3D object114.

In some embodiments, dark-field illumination is used, where a black orotherwise dark background may be placed behind the 3D object 114 (orbehind a shell or aligner). Alternatively, bright-field illumination maybe used, where the 3D object 114 (or shell or aligner) is placed betweena camera and a bright light source. In some embodiments, one or more ofthe first light source 106, second light source 112 and/or third lightsource 120 comprises a variable aperture. This may facilitate reading ofa laser marking on the 3D printed object (or shell or aligner).

The top view camera apparatus 116 may include a top view camera 118 thatis configured to capture images of the 3D object 114. The top viewcamera 118 may include a high definition camera in one embodiment. Insome embodiments, the top view camera apparatus 116 may include one ormore cameras that capture a wide field of view of the 3D printed object.The top view camera 118 may be a two-dimensional camera or a 3D camera(e.g., a pair of cameras that generate a stereo image pair, a camera andassociated structured light projector that shines a structured lightpattern onto the 3D object 114, and so on). The top view camera 118 maybe configured to acquire top view images of the 3D object 114 usingcertain illumination settings to enable the 3D object 114 to be visiblein a top view image. In one embodiment, the top view camera 118 has afixed position. Alternatively, the top view camera 118 may be a movablecamera. For example, the top view camera 118 may be moveable in the x, yand z directions and/or may rotate about one or more axes. Imagercontrol module 140 may send instructions to top view camera apparatus116 to set a zoom setting of the top view camera 118, to set an angle ofthe top view camera 118, to set a position of the top view camera 118,and so on. Instructions from the imager control module 140 may alsocause the top view camera 118 to generate one or more images of the 3Dobject 114.

The side view camera apparatus 124 may include a side view camera 126that is configured to capture images of the 3D object 114. The side viewcamera 126 may be a two-dimensional camera or a 3D camera (e.g., a pairof cameras that generate a stereo image pair, a camera and associatedstructured light projector that shines a structured light pattern ontothe 3D object 114, and so on). In one embodiment, the side view camerais a high resolution camera and/or a high speed camera (e.g., capable ofcapturing an image up to every millisecond. The side view camera 126 mayacquire multiple images of different regions of the 3D object 114 bymoving (e.g., by rotation and/or translational motion) the 3D printedobject using the multi-axis platform, which may be directed via thex-y-z-θ controls, and generating images at different rotation and/ortranslational motion settings of the multi-axis platform

The side view camera 126 may be attached to a moveable base 128.Alternatively, the side view camera may be at a fixed position, or maybe on a different type of base (which may or may not be movable). Themoveable base 128 may allow the side view camera 126 to move towards andaway from the 3D object 114, thus allowing the side view camera 126 tocapture images of the 3D object 114 from different perspectives. Themoveable base 128 may be connected to a platform 130, which guides themoveable base 128 towards and away from the 3D object 114. In oneembodiment, the platform 104 (and 3D object 114 disposed thereon) may bestationary, and the side view camera 126 may be movable around theplatform 104 (e.g., on a track that wholly or partially circumscribesthe platform 104). In one embodiment, the platform 104 is a multi-axisplatform and the side view camera 126 is movable around the platform104. In one embodiment, the side view camera 126 may capture multipleimages and/or a video of the 3D object 114 as it moves with the platform104. The video may include multiple frames, where each frame may be animage of a distinct region of the 3D printed object. Imager controlmodule 140 may send instructions to side view camera apparatus 124 toset a zoom setting of the side view camera 126, to set an angle of theside view camera 126, to set a position of the side view camera 126, andso on. Instructions from the imager control module 140 may also causethe side view camera 126 to generate one or more images of the 3D object114.

Image control module 140 may cause the top view camera 118 and/or sideview camera 126 to capture images of the 3D object 114. Image controlmodule 140 may then receive the images and process the images accordingto the methods shown in FIGS. 3A-6. In one embodiment, a firstillumination may be provided to the 3D object 114 by at least one of thefirst light source 106, the second light source 112, and/or the thirdlight source 120. One or light emitting elements 108 of the first,second, or third list sources 106, 112, 120 may be activated at the sametime, while one or more other light emitting elements 108 may not beactivated, to provide the first illumination.

The image control module 140 may control which light emitting elements108 of the first, second, and/or third light sources 106, 112, 120 areactivated and which light emitting elements 108 are not illuminated, aswell as the intensities of the various activated light emittingelements, to provide the first illumination. In one embodiment, the 3Dprinted object 113 may include a part identifier (ID), case ID, patientID and/or other ID printed on or otherwise displayed thereon.

In some embodiments, one or more of the light sources 106, 112, 120 is avariable aperture light source with at least two aperture settings. Inone embodiment, the first light source 106 has a first aperture settingof about 130 degrees (e.g., around 100-160 degrees) and a secondaperture setting of about 40 degrees (e.g., about 20-60 degrees). Thefirst aperture may be used to obtain images for reading a laser markingin the 3D object 114, because at this lighting condition laser markingsmay have good contrast (while a contour of the 3D printed object orshell or aligner may not have good contrast). The second aperture may beused for imaging used for defect detection, because the contrast ofcontours may be much better at the second aperture.

The aperture can be changed either by moving the first light source 106up and down or by splitting it in two parts. If the light source 106 issplit into multiple parts, the first part and second part may be placedapart from one another with a particular angle of orientation betweenthem. Each part may include one or more light emitting elements 108. Forexample, the first part may be placed with an orientation such that anaxis of the first part is approximately parallel with an axis of the topview camera 118. The second part may be offset from the first part, andmay be oriented such that an axis of the second part is at about a 50-80degree angle (e.g., a 65 degree angle) to the axis of the top viewcamera 118 and to the axis of the first part. To generate an image thatwill enable the contours of the 3D printed object (or the contours ofthe shell or aligner) to be determined, the first part may be activatedwithout activating the second part (causing a low apertureillumination). To generate an image that will enable the laser markingto be read, the first and second parts may both be activated (causing ahigh aperture illumination). In some embodiments, the second part mayinclude multiple light emitting elements 108 that are on three sides ofthe 3D printed object (or shell or aligner) that is to be illuminated sothat it doesn't obstruct the side camera field of view.

A first image of the 3D object 114 may be generated by at least onecamera, such as the top view camera 118, and may include an image of theID. The first image may include a symbol sequence displayed on adistinct region on the 3D object 114.

In one embodiment, the first image of the 3D object 114 may be generatedby the top view camera apparatus 116 while the first light source 106 isset to provide a wide light field. The laser marking may provide anidentification of a particular 3D printed object (or of a particularshell or aligner), and may correspond to a particular digital model ofthe 3D printed object (or of the shell or aligner). The image controlmodule may perform optical character recognition (OCR) on the symbolsequence to determine the ID. In other embodiments, a technician maymanually enter the ID associated with the 3D printed object at aninspection station using an interface, such as the user interface (UI)illustrated in FIG. 9. In other embodiments, the ID may be obtainedbased on a known order and/or position of the 3D printed object inobject sorting system. For example, a robotic object sorting system mayretrieve 3D printed objects and place them at a particular position in astaging area. A robotic arm may then retrieve the 3D printed object fromthe staging area, and may determine the ID associated with the 3Dprinted object based on the position of the 3D printed object in thestaging area. The determined ID may then be sent to the computing device135.

The imager control module 140 may associate images of the 3D object 114with the determined ID. In one embodiment, the processing logic maydetermine a digital file associated with the ID. The digital file mayinclude one or more properties associated with the 3D object 114. In oneembodiment, a first property may include a geometry associated with atleast one surface of the 3D object 114. In another embodiment, a secondproperty may include the composition of the 3D object 114. The digitalfile may further include a light source arrangement associated with atleast one of the first property and the second property. The processinglogic may cause one or more light emitting elements 108 to be activatedand one or more additional light emitting elements 108 to not beactivated based on the light source arrangement associated the firstproperty and/or the second property. For example, the first property mayinclude a shape of the 3D object 114 (e.g., as depicted in a 3D virtualmodel of the 3D printed object), and the image control module 140 maydetermine an arrangement of light sources 106, 112, 120 that willilluminate the 3D object 114 without casting shadows on portions of the3D object 114 being imaged. In another example, the second property maybe a material of the 3D object 114, which may indicate a range ofwavelengths of light for which the material is transparent ortranslucent, and image control module 140 may cause one or lightemitting elements 108 to emit light in range of wavelengths for whichthe material is transparent or translucent. For example, the imagercontrol module 140 may cause one or more light sources 106, 112, 120 toemit a wavelength associated with blue light (e.g., 450-495 nm) if the3D object 114 is composed of nylon (which is transparent for bluelight).

A plurality of images may be generated by at least one imaging device(e.g., the top view camera 118 or the side view camera 126). In oneembodiment, an image is generated by the top view camera 118 while thefirst light source 106 is set to provide a narrow light field. The imagemay then be used to determine contours of the 3D printed object (or ofthe shell or aligner). In one embodiment, a plurality of images may begenerated by the side view camera 126. Each image of the plurality ofimages may depict a distinct region of the 3D object 114. In oneembodiment, while the side view images are generated, light source 112may be activated and light source 106 may be deactivated. In someembodiments, a camera lens diaphragm is contracted (at least partiallyclosed) during generation of one or more of the side view images toachieve a large focus depth. This can facilitate obtaining a sharppicture in a side projection at large angle with vertical.

In one embodiment, the digital file may further include a motion profileassociated with at least one property included in the digital file. Theimager control module 140 may determine a degree, velocity, oracceleration of the rotation and/or translational motion of the platform104 on the platform apparatus 102 while images are captured, inaccordance with the motion profile. The imager control module 140 maydetermine the location, velocity, or acceleration of the moveable base128 on the side view camera apparatus 124 while images are beingcaptured, in accordance with the motion profile.

In some embodiments, a reference object may be used to determine arotation angle and/or a position in space of the 3D printed object (orshell or aligner) for each image. The reference object may be a circularpattern of dots 190 surrounding the 3D object 114, for example, as shownin FIG. 1C. The number of dots and the circle diameter may be selectedsuch that at least four dots are always in the top camera field of viewand the 3D object 114 rarely covers a dot in the side view. Knowing theposition of four dots or more, one can determine the camera position bya standard technique used in camera calibration. Also there may be somedots marked with additional dots of the same or smaller size outside thecircle. This will allow to determine the rotation angle exactly. A rounddot is an object that can be detected on an image quickly and reliably.

Referring back to FIG. 1A, once computing device 135 receives an imageof the 3D object 114, the image may be processed by image inspectionmodule 145. Image inspection module 145 may determine whether a receivedimage is processable. In one embodiment, image inspection module 145determines whether a contrast depicted in the first image is sufficientto enable further image processing operations (e.g., edge detection,processing by a trained machine learning model, etc.) to be performed.In one embodiment, image inspection module 145 determines a contrastmetric for the image, and determines whether the contrast metric exceedsa contrast threshold. If the contrast metric is below the contrastthreshold, the processing logic may determine the image is notprocessable. If the contrast metric exceeds the contrast threshold,image inspection module 145 may determine that the image is processable,and the image inspection module 145 may process the image using atrained machine learning model to determine whether a manufacturingdefect (e.g., a gross defect, layering defect, etc.) is present withinthe region of the 3D object 114 represented in the image. In oneembodiment, different machine learning models are used for 3D printedobjects than for shells.

If the image inspection module 145 determines that the image is notprocessable, the imager control module 140 may cause a second image ofthe same distinct region of the 3D object 114 depicted in the firstimage to be generated. Imager control module 140 may determine a secondillumination to be used on the 3D object 114 by at least one of thefirst light source 106, the second light source 112, and/or the thirdlight source 120 for the second image. The second illumination may beselected so as to provide a different light pattern, shadow patternand/or contrast on the 3D object 114 than was provided by the firstillumination.

In one embodiment, the processing logic may cause one or more lightemitting elements 108 that were not activated for the first illuminationto be activated for the second illumination. Alternatively, oradditionally, the processing logic may adjust the intensity of one ormore light emitting elements that were previously activated.

In one embodiment, the imager control module 140 may comprise a trainedmachine learning module (e.g., an artificial neural network, deep neuralnetwork, etc.) that has been trained to determine an optimalillumination for a region of a 3D object 114 (e.g., settings for one ormore light sources) based on an input of a shape of the 3D object 114(e.g., a virtual 3D model of the 3D object 114 and/or 2D image of the 3Dobject 114), and/or an angle and/or position of a camera relative to the3D object 114. The trained machine learning model may have been trainedusing a training dataset, where each data item in the training datasetmay include a) a 2D image or a virtual 3D model and camera angle, b)settings for one or more light sources 106, 112, 120, and c) anindication of sufficient illumination or insufficient illumination. The3D model of the 3D object 114 and a rotation and/or translational motionsetting of the platform 104 and/or camera position setting of the sideview camera 126 may be input into the trained machine learning module,which may output settings for the one or more light sources 106, 112,120 (e.g., indications of which light emitting elements 108 in each ofthe light sources 106, 112, 120 to activate). Alternatively, an imagethat was determined to have insufficient contrast may be input into thetrained machine learning module.

A second image may then be generated by at least one imaging device(e.g., the top view camera 118 or the side view camera 126). In oneembodiment, the second image may depict the same distinct region of the3D object 114 that was depicted in the first image that was determinedto be unprocessable. In another embodiment, the second image may depicta different distinct region of the 3D object 114 than was depicted inthe first image. In one embodiment, the trained machine learning moduleis used to determine an optimal illumination for one or more images ofthe 3D object 114 before any images are determined to be unprocessable.

In one embodiment, once an image is determined to be processable, thatimage may be input into a trained machine learning module of the imageinspection module 145 that has been trained to identify defects inimages of 3D printed objects or shells. In one embodiment, a separateoperation is not performed to determine whether an image is processableprior to inputting the image into the machine learning model trained toidentify defects. Instead, the machine learning model may output aconfidence value along with an indication of whether or not a defect hasbeen identified (and/or a type of defect that has been identified). Ifthe confidence value is below a confidence threshold, then a newillumination may be determined and a second image of the region depictedin the first image may be generated using the new illumination. Thesecond image may then be processed by the machine learning model. Themachine learning model that identifies defects in 3D printed objects isdiscussed in greater detail below.

FIG. 1B illustrates a 3D view of one embodiment of an imaging system 101that captures images of a 3D object 114 (e.g., a 3D printed object orshell), in accordance with one embodiment. The imaging system 101 mayinclude platform apparatus 102 and side view camera apparatus 124. Theimaging system 101 may also include top view camera apparatus 116, whichis not shown in FIG. 18. The imaging system 101 may be used to captureimages of a 3D object 114 being analyzed to determine whether amanufacturing defect is present in the 3D object 114 (e.g., a grossdefect, a layering defect, etc.). In the illustrated embodiment, the 3Dobject 114 is a mold of a dental arch that will be used to form analigner. As shown, the platform 104 is a circular platform that ismoveable (e.g., via a stepper motor). As also shown, the side viewcamera 126 is movable toward and away from the platform apparatus 102(e.g., via a stepper motor).

FIG. 2A illustrates an example imaging system 200 including a top viewcamera 202 and a side view camera 204, in accordance with oneembodiment. The imaging system 200 may be used to perform automateddefect detection of a 3D object 206, in accordance with one embodiment.The 3D object 206 may be a 3D printed object or a shell that was formedusing a 3D printed object. The 3D object 206 may be secured in astationary position by a platform part holder 208. A top view camera 202may be configured to acquire a top view image of the 3D object 206 usingcertain illumination settings to enable the 3D object 206 to be visiblein the top view image. Processing logic may obtain an outline of aprojection or silhouette of the 3D object 206, in accordance with anembodiment. The side view camera 204 may be used to acquire front andback side views of the 3D object by rotating around the 3D object 206 asit is held by the platform part holder 208 or by the 3D object 206 beingrotated as the side view camera 204 remains stationary. In someembodiments, the 3D object 206 may not be secured by a platform partholder 208 and the 3D object 206 may be stationary on the platform whilethe side view camera 204 takes multiple images around the sides of the3D object 206. The cameras 202 and 204 may be static and placed awayfrom a conveyor path in some embodiments. The imaged 3D object 206 maybe placed on an x-y-z-θ (4 axes of motion control) platform or stage insome embodiments.

The imaging system 200 may acquire separate front and back side viewimages without stray light interference from the side not currentlyunder inspection by using a backing screen 210 in some embodiments. Thebacking screen 210 may be inserted into the gap between the front(buccal) and back (lingual) side of 3D object 206. In some embodiments,the backing screen is a dark screen. In some embodiments, the backingscreen is a lit screen comprising one or more light emitting elements. Amotion control and screen path may be determined for the 3D object 206by identifying points between the front side and the back side of the 3Dobject that enable a screen path to be generated such that the backingscreen 210 does not touch the 3D object throughout the screen path.Processing logic may detect a center of the 3D object and adjust themotion control and screen path parameters accordingly. Further, themotion control speed may be high enough to achieve an inspection cyclewithin a target time period (e.g., 10-20 seconds) for both the frontside and the back side of the 3D object. A mirror 212 may be used as adeflector to capture the images from the back side or the front side ofthe plastic aligner as it is held in the platform part holder 208 inembodiments. The mirror 212 may be angled at a certain degree (e.g.,45°, 50°, 55°, etc.) and may be used in combination with a light sourceto enable images to be captured that profile particular regions of the3D object 206 in some embodiments.

In some embodiments, the imaging system 200 may not use a backing plate.In some embodiments, the imaging system 200 can use a focused light toilluminate the 3D object 206. In an embodiment, the top view camera 202may capture a top view image of the 3D object 206, and a top viewcontour may be extracted. In some embodiments, the 3D object 206 may beplaced within the field of view of the top view camera 202 and theimaging system 200 may align the 3D object 206 to capture the top viewimage.

The top view image may be used to determine an inspection recipeincluding one or more side view images in some embodiments. Using thetop view contour, contour x-y points may be transmitted to the side viewcamera 204. In some embodiments, properties of the camera, such as zoomand/or focus depth may be determined for the inspection recipe dependingon which side of the 3D object 206 is being captured. For example, ifthe 3D object is an aligner configured to fit over a dental arch, or ifthe 3D object is a mold of a dental arch, the focus area of the sideview camera 204 may be adjusted to focus on the 3D object depending onwhether a lingual side or buccal side is being imaged. Further, the topview image may be used to rotate the 3D object 206 to proper orientationso the region under inspection is facing the side view camera 204. Insome embodiments, the rotary motion required to rotate the 3D object 206may occur simultaneously with the x-y motion of the side view camera 204and may not affect inspection time.

An x-y-rotary stage motion control system or a multi-axis robot arm maybe used to adjust the orientation and/or position of the camera and/orto adjust the orientation and/or position of the platform in order toacquire the appropriate images. In some embodiments, the 3D object 206may rest on a glass platform and a cylinder of light may illuminate frombelow the glass platform.

The top view image and/or the side view images may be input into atrained machine learning model to determine whether any defects areincluded in the 3D object.

FIG. 2B illustrates an example imaging system 250 including a top viewcamera 256 and a side view camera 258, in accordance with oneembodiment. The imaging system 250 may be used to perform automateddefect detection of a 3D object 270, in accordance with one embodiment.The 3D object 270 may be a 3D printed object or a shell that was formedusing a 3D printed object. The 3D object 270 may be disposed on aplatform 254, which may be a rotatable platform. In one embodiment, theplatform 254 is an x-y-z-θ (4 axes of motion control) platform. Top viewcamera 256 may be configured to acquire one or more top view image ofthe 3D object 270 using certain illumination settings of light source260A and/or light source 260B.

Light source 260A and light source 260B may together form a variableaperture light source 260 with at least two aperture settings. In oneembodiment, the variable aperture light source 260 has a first aperturesetting of about 130 degrees (e.g., around 100-160 degrees) and a secondaperture setting of about 40 degrees (e.g., about 20-60 degrees). Thefirst aperture may be used to obtain images for reading a laser markingin the 3D object 270. The second aperture may be used for imaging usedfor defect detection.

Light source 260A may be a first part of the variable aperture lightsource 260, and light source 260B may be a second part of the variableaperture light source 260. The first and second light sources 260A, 260Bmay be placed apart from one another with a particular angle oforientation between them. Each light source 260A, 260B may include oneor more light emitting elements. In one embodiment, the light source260A may be placed with an orientation such that an axis of the lightsource 260A is approximately parallel with an axis of the top viewcamera 256. The light source 260B may be offset from the light source260A, and may be oriented such that an axis of the light source 260B isat about a 50-80 degree angle (e.g., a 65 degree angle) to the axis ofthe top view camera 256 and to the axis of the light source 260A. Togenerate an image that will enable the contours of the 3D object 270 tobe determined, the light source 260A may be activated without activatingthe light source 260B (causing a low aperture illumination). To generatean image that will enable the laser marking to be read, the light source260A and light source 260B may both be activated (causing a highaperture illumination). Processing logic may obtain an outline of aprojection or silhouette of the 3D object 270, in accordance with anembodiment.

The side view camera 204 may be used to acquire front and back sideviews of the 3D object by rotating around the 3D object 270 as it isheld by the platform 254 or by the 3D object 270 being rotated as theside view camera 258 remains stationary. In some embodiments, lightsource 260B is activated, but light source 260A is not activated, forgenerating side view images by side view camera 258. In someembodiments, an axis of light source 260B is approximately parallel toan imaging axis of side view camera 258.

The top view image and/or the side view images may be input into atrained machine learning model to determine whether any defects areincluded in the 3D object.

FIGS. 3A-6 are flow diagrams showing various methods for performingautomated defect detection of a 3D printed part or a shell formed over a3D printed mold (and optionally subsequently removed therefrom), inaccordance with embodiments of the disclosure. Some operations of themethods may be performed by a processing logic that may comprisehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processingdevice to perform hardware simulation), or a combination thereof. Theprocessing logic may execute on one or many processing devices (e.g., ofcomputing device 135 of FIG. 1A). The processing logic may be processinglogic of image inspection module 145 and/or of imager control module 140in embodiments. Some operations of the methods may be performed by animaging system, such as imaging system 101 of FIGS. 1A-B, or imagingsystem 200 of FIG. 2.

For simplicity of explanation, the methods are depicted and described asa series of acts. However, acts in accordance with this disclosure canoccur in various orders and/or concurrently, and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be required to implement the methods in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methods could alternatively berepresented as a series of interrelated states via a state diagram orevents.

FIG. 3A illustrates a flow diagram for a method 300 of detecting amanufacturing defect in a 3D printed object or a 3D shell, in accordancewith one embodiment. One or more operations of method 300 may beperformed by a processing logic of a computing device. It should benoted that the method 300 may be performed for multiple unique 3Dprinted objects or unique 3D shells. In one embodiment, the method 300may be performed for each unique mold for each stage of a patient'sorthodontic treatment plan, where each unique mold is used to form acustomized aligner for a particular stage of the orthodontic treatmentplan. In a further embodiment, the method 300 may be performed for eachunique aligner for each patient's treatment plan, where each uniquealigner is customized for a stage of the treatment plan. The aligner maybe a 3D printed aligner, or may have been formed by thermoforming asheet of plastic over a 3D printed mold of a dental arch. Method 300 isdescribed with reference to a 3D printed object. However, in embodimentsmethod 300 may instead be performed for a shell that was formed using a3D printed mold (e.g., of a dental arch).

At block 302, a first illumination of a 3D printed object may beprovided using a first light source arrangement (e.g., a first set oflight emitting elements from a first light source, second light sourceand/or third light source). The first illumination may be provided by animaging system (e.g., by the imaging system depicted in FIGS. 1A and 1B,or by the imaging system depicted in FIG. 2A or FIG. 2B) based oninstructions from processing logic. In one embodiment, the firstillumination may be provided by light emitting elements from at leastone of a first light source or a second light source included on aplatform apparatus, and/or light emitting elements from a third lightsource that may be included on a top view camera apparatus. A processinglogic may determine which light emitting elements from the one or morelight sources to activate and which light emitting elements not toactivate, in accordance with first illumination settings, and may sendinstructions to the light sources to cause the determined light emittingelements to activate.

At block 304, a plurality of images of the 3D printed object may begenerated using one or more imaging devices (e.g., side view cameraand/or top view camera) based on instructions from processing logic.

In one embodiment, at least one image is generated by a top view cameraprovided by the imaging system depicted in FIG. 1A or FIG. 2A or FIG.2B. The first image may then be received and processed by the processinglogic.

In one embodiment, the first image is used to select an illuminationsetting for the generation of additional images, as set forth in FIG. 4.The first image may include a representation of an ID (e.g., symbolsequence) on a distinct region of the 3D printed object. In oneembodiment, the first image was generated under particular lightingconditions that increase a clarity and/or contrast of laser markings(e.g., where the representation of the ID is a laser marking), asdescried above. In some embodiments, the processing logic may performoptical content recognition (OCR) on the first image to identify thesymbol sequence to determine the ID, in accordance with the methoddepicted in FIG. 3. The ID may be associated with the 3D printed objectdepicted in the first image. The processing logic may determine adigital file associated with the ID. The digital file may include one ormore properties associated with at least one surface of the 3D printedobject. The digital file may further include a light source arrangement(illumination setting) associated with the first property and/or thesecond property. The processing logic may cause illuminated particularillumination based on the light source arrangement associated with thedigital file. In another embodiment, the processing logic may cause oneor more light emitting elements to emit a specified wavelength of light.

The first image may be used to determine gross defects in the 3D printedobject, as set forth in method 500 of FIG. 5 in some embodiments.

At least one image of the plurality of images of the 3D printed objectmay be generated by a side view camera provided in the imaging systemdepicted in FIGS. 1A and 1B, or by the imaging system depicted in FIG.2A or FIG. 2B. The side view camera may be configured to capture sideviews of the 3D printed object. Each image of the plurality of imagesmay depict a distinct region of the 3D printed object (e.g., a tooth ofa mold for an orthodontic or polymeric aligner), and may be generatedusing a combination of one or more of a particular zoom setting, aparticular angle setting of the side view camera, a particular position(e.g., x-coordinate) setting of the side view camera, a particularrotation and/or translational motion setting of a platform supportingthe 3D printed object, particular x,y,z,θ settings of the platform, andso on.

In some embodiments, the platform 104 may be in motion while images aregenerated of the 3D printed object. For example, the platform 104 maymove on at least one axis of motion and/or rotate while images or avideo are generated. The processing logic may determine a degree,velocity, or acceleration of the rotation and/or translational motion ofthe platform while images are captured. In another embodiment, the sideview camera may be attached to a moveable base. The processing logic maydetermine the location, velocity, or acceleration of the moveable basewhile images are being captured. In one embodiment, the side view camerais a high speed vision camera that is capable of generating an imageevery millisecond. In one embodiment, all images of the 2D printedobject may be generated within a matter of seconds (e.g., in about 2seconds). At block 305, an image is selected from the plurality ofimages.

At block 306, the processing logic may determine whether the image isprocessable by a machine learning model. In one embodiment, theprocessing logic may determine whether the image is processable based ona contrast of the image. Processing logic may generate a contrast metricfor the image. The processing logic may then compare the contrast metricwith a contrast threshold. If the contrast metric falls below thecontrast threshold, the processing logic may determine the first imageis not processable by the machine learning model, and the method maycontinue to block 310. If the contrast metric does not fall below thecontrast threshold, the processing logic may determine that the image isprocessable by the machine learning model and the processing logic maycontinue to block 308. In some operations, the operations of block 306are omitted.

At block 310, the processing logic may provide a second illumination tothe 3D printed object using a second light source arrangement. Toprovide the second illumination, processing logic may determine a newset of light emitting elements from a first light source, second lightsource and/or third light source that provides a different illuminationthan the first illumination. In one embodiment, the processing logic mayinput data into a machine learning model to determine an illuminationsetting for the second illumination, as set forth above. Processinglogic may then instruct the one or more light sources as to which lightemitting elements to activate. The second illumination may be selectedso as to provide a different light pattern, shadow pattern, contrast,etc. on the 3D printed object than provided by the first illumination.

At block 312, a new version of the unprocessable image may be generated.The new version of the unprocessable image may include a new imagedepicting the same distinct region of the 3D printed object depicted inthe unprocessable image. In one embodiment, processing logic determinesone or more settings of the multi-axis platform and/or the side viewcamera that were used to generate the selected image, and causes themulti-axis platform and the side view camera to have the same or similarsettings for the generation of the new version of the unprocessableimage. In one embodiment, the new image may depict a different distinctregion of the 3D printed object that was depicted in the unprocessableimage. For example, the new image may depict a zoomed in view of thedistinct region that was depicted in the unprocessable image. The newimage may be generated by at least one imaging device (e.g., the topview camera or the side view camera). After the new image is generated,the processing logic may replace the unprocessable image in theplurality of images with the new version of the unprocessable image.

After the new version of the unprocessable image is generated and hasreplaced the unprocessable image of the plurality of images, the methodwill return to block 305, where the new image will eventually beselected at block 305 to determine whether the image is processable bythe machine learning model.

If the processing logic determines that an image selected at block 305is processable by the machine learning model, the method will continueto block 308, where the image is processed by the machine learning modelin accordance with the method of FIG. 6. The machine learning model maybe trained to receive an image of a 3D printed object or a shelloriginally formed over a 3D printed object as an input, and to provideas an output an indication of one or more types of defects. In oneembodiment, the machine learning model outputs a classification thatindicates whether or not a defect has been detected. In one embodiment,the machine learning model may identify multiple different types ofdefects, and indicate for each type of defect whether that type ofdefect has been identified in the image. For example, the output mayinclude a vector having multiple elements, where each element mayinclude a value representing a probability that the image contains adefect of a particular defect type. In a further example, an output mayindicate a 90% probability of an internal volume defect, a 15%probability of a surface defect, a 2% probability of an interfacedefect, a 5% probability of a line thickness defect, and an 8% chance ofa delamination defect. Defect probabilities that are above a threshold(e.g., 80% probability, 90% probability, etc.) may be classified asdefects. Defect probabilities that are below the threshold may beclassified as defect-free.

In one embodiment, in addition to identifying the presence of defects,the machine learning model also outputs coordinates associated with theidentified defect. The coordinates may be x, y pixel locations in theinput image. Processing logic may then mark the image with thecoordinates of the defect in some embodiments. Additionally, processinglogic may indicate a type of defect identified at the identifiedcoordinates in embodiments. This may enable a user to quickly review anddouble check results of positive defect classifications.

In one embodiment, the machine learning model may output a confidencemetric for each defect probability that it outputs. The confidencemetric may indicate a confidence level associated with the output defectprobability. If a confidence metric is low, this may indicate that therewas insufficient detail and/or contrast in the image to accuratelydetermine whether a defect exists. In some embodiments, the confidencemetric output by the machine learning model is compared to a confidencethreshold. If the confidence metric is below the confidence threshold,then the method may proceed to block 310 (not shown in figure). This maybe performed instead of or in addition to the operations of block 306.

In one embodiment, the machine learning model may output a defect ratingfor each defect or group of defects identified in an input image. Thedefect rating may rate a manufacturing defect in accordance with theseverity of the defect and/or the likelihood that the defect will latercause a deformation or a malfunction. The defect rating may be based ona density or quantity of defects identified in the image as well assizes of the defects and/or the types of defects. At block 314, theprocessing logic may determine whether all of the images generated havebeen processed. If any of the images from the plurality of images havenot yet been processed, the method returns to block 305, and a new imageis selected. Note that the operations of block 305-314 may begin beforeall images of a 3D printed object have been generated. For example,imaging system 101 may begin taking images of a 3D printed object, andprocessing logic may start processing a first one or more images whilethe imaging system 101 continues to generate additional images. In someembodiments, multiple threads may be instantiated, and each thread mayprocess one or more images in parallel to speed up the defect detectionprocess. Once all images have been processed, the method continues toblock 316.

At block 316, the processing logic may determine whether anymanufacturing defects are identified in any of the images. Theprocessing logic may evaluate each output generated by the machinelearning model for each image of the plurality of images. In oneembodiment, the processing logic may determine a manufacturing defect isidentified in the images if at least one defect is identified in atleast one image of the plurality of images by the machine learningmodel. In one embodiment, the processing logic determines a combineddefect rating for images of the 3D printed object. The combined defectrating may be based on the defect ratings associated with each of theimages. Processing logic may then determine whether the combined defectrating exceeds a defect threshold.

In one embodiment, the machine learning model may modify the defectrating for a defect based on the location or severity of other defectsidentified in another image of the plurality of images. For example, themachine learning model may have generated a low defect rating for adefect identified in a first image of a 3D printed object and a highdefect rating for a defect identified in a second image of the 3Dprinted object. The processing logic may determine that the defectidentified in the first image could contribute to a deformation ormalfunction caused by the defect identified in the second image, and mayadjust a rating of the defect identified in the first image to a highdefect rating.

If the combined defect rating falls below the defect threshold, theprocessing logic may determine that a manufacturing defect is notidentified in the plurality of images associated with the 3D printedobject, and the method 300 may continue to block 320. If the combineddefect rating exceeds the defect threshold, the processing logic maydetermine that a manufacturing defect is identified in the plurality ofimages associated with the 3D printed object, and the method 300 maycontinue to block 318.

At block 318, the processing logic may fail the 3D printed object basedon the output provided by the machine learning model (the 3D printedobject may fail quality control and be identified as defective). In oneembodiment, the failed 3D printed object may be fixed to remove themanufacturing defect. In a further embodiment, the 3D printed object maybe prevented from being used further in a manufacturing process orshipped to a user. In another embodiment, the 3D printed object may bescrapped and a replacement may be manufactured.

A record may be kept of which 3D printers manufacture each 3D printedobject output by a manufacturer or location. For example, a database maybe maintained that includes a separate entry for each 3D printed object.One field of an entry may be an ID of the 3D printed object. Anotherfield may be a patient ID. Another field may be a treatment plan ID.Another field may be a 3D printer ID identifying the 3D printer of that3D printed object. In a further embodiment, the processing logic mayidentify a 3D printer that manufactured the failed 3D printed object(e.g., based on the 3D printer ID associated with the 3D printedobject). The processing logic may determine other 3D printed objectsthat were printed by that 3D printer, and may determine whether the 3Dprinter that manufactured the failed 3D printed object manufactured oneor more additional 3D printed objects that also included manufacturingdefects (and may have failed quality inspection as well). The combinedinformation of the 3D printed objects that have failed quality controland/or their defects may be used to determine whether to schedulemaintenance on the 3D printer. For example, a trend of increasingamounts of defects in 3D printed objects manufactured by a particular 3Dprinter may be identified, and maintenance may be scheduled for the 3Dprinter based on the trend. In one embodiment, a defect score iscomputed for the 3D printer based on the defects in 3D printed objectsmanufactured by the 3D printer. If the defect score exceeds a threshold,then maintenance may be scheduled for the 3D printer. In one embodiment,a trained machine learning model (e.g., a recurrent neural network(RNN)) is trained to determine when to schedule maintenance for a 3Dprinter based on defects of 3D objects manufactured by that 3D printer.For example, combined defect results for each 3D printed objectmanufactured by the 3D printer may be input into the RNN over time. TheRNN may determine when a pattern in increasing amounts of defectsindicates that maintenance should be scheduled, and may output arecommendation of scheduling maintenance. Processing logic may thenschedule maintenance on the 3D printer so to prevent manufacturingdefects from forming on additional 3D printed objects based on theoutput of the RNN.

At block 320, the processing logic passes the 3D printed object based onthe outputs provided by the machine learning model at block 308. If fewor no manufacturing defects are identified in one or more of the images,for example, the 3D printed object may pass quality control and beidentified as acceptable.

FIG. 3B illustrates a flow diagram for a method 330 of determining an IDassociated with a 3D printed object or a shell, in accordance with oneembodiment. At block 332, a first illumination of a 3D printed object ora shell may be provided using a first light source arrangement. Thefirst light source arrangement may be provided by the imaging systemdepicted in FIGS. 1A and 1B, or by the imaging system depicted in FIG.2A or FIG. 2B. At block 334, an image of the 3D printed object may begenerated using one or more imaging devices (e.g., top view cameraand/or side view camera). In one embodiment, the image is generated by atop view camera provided by the imaging system depicted in FIG. 1A. Inone embodiment, the image is generated by a camera of the imaging systemdepicted in FIG. 2. At block 336, OCR is performed on the image. Theprocessing logic may process the image to identify a location of asymbol sequence in the image. The symbol sequence may contain letters,numbers, special symbols, punctuation symbols, etc. The processing logicmay then perform OCR on the symbol sequence. The OCR may be performedusing any known method, such as matrix matching, feature extraction, ora combination thereof. In one embodiment, the processing logic may applyone or more conversion operations to the image to more clearly processthe symbol sequence. Conversion operations may include changing theresolution of the image, performing binarization of the image usingcertain parameters, correction of distortions, glare, or fuzziness,performing noise reduction operations, etc. These operations may beapplied to the entire image or the portion of the image containing theidentified symbol sequence.

In one embodiment, the symbol sequence may not be displayed entirely bythe image generated by the imaging system. For example, the image maydepict a distinct region of the 3D printed object or the shellcontaining one half of the symbol sequence. In one embodiment, theprocessing logic may generate at least one additional image of thelocation of the symbol sequence. If the newly generated image includesthe entire symbol sequence, the processing logic may process the imagein accordance with the procedure described above. If the newly generatedimage depicts another half of the symbol sequence, processing logic mayperform a stitching operation to generate a single image containing thesymbol sequence. The processing logic then may perform the OCR accordingto the procedure described above.

At block 338, the ID associated with the 3D printed object or the shellmay be determined based on a result of the OCR. After the OCR isperformed according to block 336, the processing logic may produce afirst result containing a computer-readable version of the symbolsequence identified in the image. The processing logic may compare thefirst result to one or more 3D printed object IDs (or shell IDs) todetermine whether the first result corresponds to a known 3D printedobject ID or a known shell ID. If the first result is not determined tocorrespond to a known 3D printed object ID or a known shell ID, thefirst result is rejected. In one embodiment, a second OCR operation maybe performed on the image to generate a second result. In anotherembodiment, an OCR operation may be performed on a different image thanthe image that generated the first result in order to generate a secondresult. If the first or second result is determined to correspond to aknown 3D printed object identifier or a known shell identifier, theprocessing logic determines the first or second result is the IDassociated with the 3D printed object or the shell.

In another embodiment, a technician may manually input the ID associatedwith the 3D printed object or the shell at an inspection station usingan interface, such as the UI illustrated in FIG. 9. In anotherembodiment, a 3D printed object and/or shell sorting system may sort aseries of 3D printed objects and/or shells in a known order. Theprocessing logic may retrieve the 3D printed object order (or the shellorder) from the 3D printed object and/or shell sorting system in orderto know which of the 3D printed objects or shells are currently beingprocessed and the order in which they arrived at the imaging system.Optionally, the 3D printed object or the shell may arrive at the imagingsystem in a tray carrying the 3D printed object identifying informationor shell identifying information (e.g., RFID tag, barcode, serialnumbers, etc.), which is read by the imaging system depicted in FIGS. 1Aand 1B, or by the imaging system depicted in FIG. 2A or FIG. 2B.Thereafter, the processing logic may retrieve a digital file associatedwith the 3D printed object or the shell based on the 3D printed objectidentification information or the shell identification information, inaccordance with the method described for FIG. 4.

FIG. 4 illustrates a flow diagram for a method 400 of determining afirst illumination for capturing an image of a 3D printed object, inaccordance with one embodiment. At block 402, processing logic maydetermine an ID associated with a 3D printed object. The ID may bedetermined by any of the ID determination methods described for themethod depicted in FIG. 3.

At block 404, a digital file associated with the 3D printed object maybe determined, based on the ID associated with the 3D printed object. Insome embodiments, the digital file may be associated with a mold that iscustomized for a dental arch of a patient and undergoing inspection. Insome embodiments, the digital file may be associated with an alignerthat is customized for a dental arch of a patient and undergoinginspection. The digital file may include a digital model of a mold thatis customized for a dental arch of a patient. The digital file may alsoinclude a digital model of an aligner that is customized for a dentalarch of a patient. In one embodiment, the digital model of the alignermay be generated based on the manipulation of a digital model of a moldof a patient's dental arch at a stage of orthodontic treatment.

At block 406, a geometry associated with at least one surface of the 3Dprinted object may be determined, based on the digital file. In oneembodiment, the geometry may be determined based on the digital model ofa mold that is associated with the digital file. In another embodiment,the geometry may be determined based on the digital model of a shellthat is associated with the digital file.

At block 408, a first light source arrangement to provide a firstillumination is selected based on the geometry associated with at leastone surface of the 3D printed object. In one embodiment, the first lightsource arrangement based on the geometry may be included in the digitalfile. In one embodiment, the first light source is determined byinputting the digital model of the 3D printed object into a machinelearning model trained to optimal determine illumination for 3D printedobjects. In one embodiment, the machine learning model determinesdifferent optimal illumination (e.g., sets of light emitting elements toactivate) for different camera angles relative to the 3D printed object.Accordingly, the output may include multiple different illuminationsettings, where each illumination setting may correspond to a particularcamera angle relative to the 3D printed object (e.g., a particularrotation and/or translational motion setting of the multi-axisplatform). The processing logic may cause one or more light emittingelements of a first, second, and/or third light source of an imagingsystem to be activated based on the first light source arrangement(illumination setting).

In a further embodiment, a composition or material associated with the3D printed object may be determined based on the digital file. Inanother embodiment, the first light source arrangement may be modifiedbased on the composition or material associated with the 3D printedobject. For example, the processing logic may cause one or more lightemitting elements that emit a particular wavelength of light to beactivated, where the composition or material may be transparent ortranslucent to the particular wavelength of light.

FIG. 5 illustrates a flow diagram for a method 500 of detecting a grossdefect of a 3D printed object, in accordance with one embodiment. Method500 may also be performed to determine a gross defect of a shell thatwas thermoformed over a 3D printed mold. In one embodiment, a grossdefect on a mold of a dental arch used to form an orthodontic and/orpolymeric aligner, or on a directly printed orthodontic and/or polymericaligner, may include arch variation, deformation, bend (compressed orexpanded), cutline variations, webbing, trimmed attachments, missingattachments, burrs, flaring, power ridge issues, material breakage,short hooks, and so forth. At block 502, an ID associated with the 3Dprinted object is determined. The ID may be determined using any of theID determination methods described for the method depicted in FIG. 3.

At block 504, a digital file associated with the 3D printed object maybe determined. The digital file may be determined from a set of digitalfiles. The digital file may be associated with the 3D printed objectbased on the ID. Each digital file of the set of digital files mayinclude a digital model (e.g., a virtual 3D model) of the 3D printedobject. In one embodiment, each digital file may include a digital modelof a mold used to manufacture an aligner. Each digital file may be for aunique, customized 3D printed object. In one embodiment, each digitalfile may be for a specific mold customized for a specific patient at aparticular stage in the patient's treatment plan.

In one embodiment, the 3D printed object may be a directly fabricatedaligner. In a further embodiment, the digital file associated with theID may include a digital model of a first aligner that is dynamicallygenerated by the processing logic or that is received from anothersource. The digital model of the first aligner may be dynamicallygenerated by manipulating a digital model of a mold used to manufacturethe first aligner. The digital model of the first aligner may begenerated by simulating a process of thermoforming a film over a digitalmodel of the mold by enlarging the digital model of the mold into anenlarged digital model (e.g., by scaling or inflating a surface of thedigital model). Further, generation of the digital model of the firstaligner may include a projection of a cutline onto the enlarged digitalmodel, virtually cutting the enlarged digital model along the cutline tocreate a cut enlarged digital model, and selecting the outer surface ofthe cut enlarged digital model. In one embodiment, the digital model ofthe first aligner comprises an outer surface of the first aligner, butdoes not necessarily have a thickness and/or does not comprise an innersurface of the first aligner, though it may include a thickness or innersurface in other embodiments.

In one embodiment, the digital file may include a virtual 3D model of amold that is used to manufacture the first aligner. In one embodiment,the digital file may include multiple files associated with the firstaligner, where the multiple files include a first digital file thatcomprises a digital model of the mold and a second digital filecomprises a digital model of the first aligner. Alternatively, a singledigital file may include both a digital model of the mold and a digitalmodel of the first aligner.

At block 506, the processing logic may determine a first silhouette forthe first 3D printed object from the first digital file. In oneembodiment, the first silhouette is included in the first 3D printedobject. In one embodiment, the first silhouette is based on a projectionof the digital model of the first 3D printed object onto a plane definedby an image of the first 3D printed object (e.g., an image generated bythe top view camera). In one embodiment, the first silhouette is basedon a manipulation of a digital model the first 3D printed object. Forexample, in some embodiments, the first silhouette may be based on aprojection of the digital model of the 3D printed object onto the planedefined by the image of the 3D printed object. In such instances, theprojection of the 3D printed object may be scaled or otherwise adjustedto approximate a projection of a 3D printed object from a particularpoint of view (e.g., the point of view of the top view camera). In afurther embodiment, the first silhouette may be based on a manipulationof the digital model, wherein the manipulation causes an outer surfaceof the digital model to have an approximate shape of the 3D printedobject, and is further based on a projection of the outer surface of thedigital model onto the surface defined by the image of the 3D printedobject. In some embodiments, the first silhouette may be determined froman approximated outer surface of the 3D printed object. In someembodiments, the first silhouette may include a first shape of aprojection of the outer surface of the first 3D printed object onto aplane defined by an image of the 3D printed object.

At block 508, a second silhouette of the 3D printed object may bedetermined from at least one image of the 3D printed object. An image ofthe 3D printed object may define a plane. The second silhouette mayinclude an outline of a second shape of the 3D printed object asprojected onto the plane defined by the 3D printed object. The secondsilhouette may be determined directly from one or more images (e.g., topview, side view, etc.). In one embodiment, a contour of the second shapeis drawn from the image to form the second silhouette (e.g., based onperforming edge detection on the image to identify the contour).

At block 510, the processing logic may compare the first silhouette tothe second silhouette. The processing logic may identify, based oncomparing the first silhouette to the second silhouette, one or moredifferences between the first silhouette and the second silhouette. Insome embodiments, the processing logic may identify the one or moredifferences by determining one or more regions where the first shape ofthe first silhouette and a second shape of the second silhouette do notmatch. The processing logic may further determine the differences of theregions (e.g., at least one of a thickness of the one or more regions oran area of the one or more regions).

At block 512, the processing logic may generate a difference metricbetween the first silhouette and the second silhouette based on thecomparison done at block 510. The difference metric may include anumerical representation of the differences between the first silhouetteand the second silhouette.

At block 514, the processing logic may determine whether the differencemetric determined at block 512 exceeds a difference threshold. Thedifference threshold may be any suitable configurable amount (e.g.,difference greater than three millimeters (mm), 5 mm, 10 mm, a regionhaving an area greater than one hundred mm squared, etc.). If thedifference metric exceeds the difference threshold, at block 516, theprocessing logic may classify the 3D printed object as having a grossdefect. In one embodiment, the 3D printed object classified as having agross defect may be further classified as deformed. If it is determinedthat the difference metric does not exceed the difference threshold, theprocessing logic may determine that the shape of the 3D printed objectdoes not have a gross defect and may proceed to perform other methods inaccordance with this disclosure, such as the method disclosed in FIG. 6(e.g., layering defect detection).

If it is determined that the difference metric exceeds the differencethreshold, at block 516, it is determined that the 3D printed objectincludes a gross defect. In one embodiment, a 3D printed object with agross defect may be fixed so as to remove the gross defect. In anotherembodiment, a 3D printed object with a gross defect may be scrapped anda replacement 3D printed object may be manufactured prior to use orshipment of the 3D printed object.

If it is determined that the different metric does not exceed thedifference threshold, the method terminates. In one embodiment, if theone or more differences do not exceed the difference threshold, then theprocessing logic may perform additional comparisons. In one embodiment,the processing logic may perform additional comparisons for molds or foraligners. FIG. 6 illustrates a flow diagram for a method 600 ofprocessing an image captured by an imaging system to detect a defect(e.g., a fine defect such as a layering defect) on or in a 3D printedobject, in accordance with one embodiment. In one embodiment, the method600 may be performed for each unique mold for each patient's treatmentplan, where each unique mold is customized for one or more stages (e.g.,key stages) of the treatment plan. In a further embodiment, the method600 may be performed for each unique aligner for each patient'streatment plan, where each unique aligner is customized for one or morestages (e.g., key stages) of the treatment plan.

At block 602, an image of a 3D printed object is obtained by theprocessing logic. The image may have been generated as one of aplurality of images by imaging system 101 depicted in FIGS. 1A, 1B, 2Aand/or 2B. In one embodiment, the image may be generated by a top viewcamera provided by the imaging system 101. The top view camera may beconfigured to acquire top view images of the 3D printed object. Inanother embodiment, at least one image may be generated by a side viewcamera provided in the imaging system 101 depicted. The side view cameramay be configured to capture side views of the 3D printed object. Eachimage of the plurality of images may depict a distinct region of the 3Dprinted object (e.g., a tooth of a mold for an orthodontic or polymericaligner).

At block 604, edge detection (or other differencing process) isperformed on the image to determine a boundary of the 3D printed objectin the image. The edge detection may include application of an automatedimage processing function, such as an edge detection algorithm. Oneexample edge detection operation or algorithm that may be used ismultiscale combinatorial grouping. Other examples of edge detectionalgorithms that may be used are the Canny edge detector, the Dericheedge detector, first order and second order differential edge detectors(e.g., a second order Gaussian derivative kernel), a Sobel operator, aPrewitt operator, a Roberts cross operator, and so on. A segmentationoperation (e.g., a tooth segmentation operation) may also be performedon the image instead of, or in addition to, the edge detection. In oneembodiment, a segmentation operation may be applied to segment the 3Dprinted object into separate objects, so as to highlight distinctregions of the 3D printed object depicted in the image. In oneembodiment, a combination of multiple edge detection algorithms and/orsegmentation algorithms is used for edge detection.

At block 606, a set of points may be selected on the boundary. In oneembodiment, one portion of the image within the boundary may includemore contrast than another portion of the image within the boundary. Insuch embodiment, the set of points may be selected on the boundarytowards the portion of the image with more contrast. For example, if theimage depicts a mold of an orthodontic or polymeric aligner, theboundary may indicate a tooth of the mold within the area of theboundary. The portion of the tooth towards the top or crown of the toothmay include more contrast than the portion of the tooth towards thebottom of the crown. Therefore, the set of points may be selectedtowards the top or crown of 3D printed object depicted in the image.

At block 608, an area of interest is determined using the set of points.In one embodiment, the processing logic may generate a plurality ofshapes that correspond to the set of points. The processing logic mayapply each shape of the plurality of shapes to the set of points. Foreach shape applied to the set of points, the processing logic maygenerate a rating, wherein the rating is based on the number of pointsthe shape is able to connect to with. The processing logic may determinethe area of interest is included within the area of the shape with thehighest rating.

In one embodiment, the processing logic may further define the area ofinterest. The processing logic may identify a portion of the imagewithin the geometric shape to be the area of interest. In oneembodiment, the processing logic may determine the area of interestincludes a height between 0.5 mm and 1.5 mm and a width between 1.5 mmand 4.5 mm. In another embodiment, the processing logic may determinethe area of interest includes a height of 1 mm and a width of 3 mm.

At block 616, the image may be cropped to exclude the region of theimage outside of the area of interest.

At block 618, the cropped image (or uncropped image) may be processedusing a machine learning model (e.g., an artificial neural network) toidentify manufacturing defects of a 3D printing process. In someembodiments, the model may be a trained machine learning model.Alternatively, or additionally, one or more image processing operationsmay be performed on the cropped image (or the uncropped image), and aresult of the image processing operations may be compared to a set ofdefined rules.

In one embodiment, the machine learning model (or set of defined rules)may determine whether a particular type of layering defect is present byidentifying a plurality of lines present within the area of interest.The plurality of lines may result from the manufacturing process (e.g.,SLA) used to fabricate the 3D printed object. The set of defined rulesmay include an acceptable threshold number of lines that should bepresent in an area having a similar dimension to the area of interest.The processing logic may determine whether the number of lines withinthe area of interest is within the acceptable threshold. If theprocessing logic determines that the number of lines is not within theacceptable threshold, the machine learning model may indicate the areaof interest as containing a defect. The processing logic mayadditionally determine the severity of the defect and the likelihoodthat the layering defect could cause significant deformations. If theprocessing logic determines that the number of lines is within theacceptable threshold, the processing logic may indicate the area ofinterest as not containing the particular type of layering defect.

In another embodiment, the set of defined rules may include anacceptable threshold of distance between lines, where each linerepresents a different layer generated during the 3D printing process.The processing logic may determine whether the distance between thelines within the area of interest is within the acceptable threshold. Ifthe processing logic determines that the distance between lines is notwithin the acceptable threshold, the processing logic may indicate thearea of interest as containing a layering defect. The processing logicmay additionally determine the severity of the defect and the likelihoodthat the defect could cause significant deformations. If the processinglogic determines the distance between lines is within the acceptablethreshold, the processing logic may indicate the area of interest as notcontaining a layering defect.

In another embodiment, the processing logic may determine whether a gapexists between the lines within the area of interest. If a gap existsbetween the lines, the processing logic may indicate the area ofinterest as containing a layering defect (e.g., an internal volumedefect). The processing logic may additionally determine the severity ofthe layering defect (e.g., the amount of delamination) and thelikelihood that the layering defect could cause significantdeformations. If a gap does not exist between the lines, the processinglogic may indicate the area of interest as not containing a layeringdefect.

The machine learning model may be trained to identify each of the abovediscussed types of defects based on a training dataset with labeledimages of 3D printed objects that are defect free as well as labeledimages of 3D printed objects that include these types of defects.Additionally, the machine learning model may be trained to identifyother types of defects (e.g., other types of layering defects). Forexample, the machine learning model may determine whether debris, airbubbles (voids) or holes (pitting) are present within the area ofinterest. If debris or holes are present, the machine learning model mayindicate the area of interest as containing a layering defect (e.g., asurface defect or an interface defect), and may optionally indicate thetype of layering defect and/or the coordinates on the image where thelayering defect was detected. The machine learning model mayadditionally determine a severity of the layering defect and thelikelihood that the layering defect could cause significantdeformations. If debris or holes are not present, the machine learningmodel may indicate the area of interest as not containing a layeringdefect.

The machine learning model may generate an output to be processed by theprocessing logic. In one embodiment, the output to the machine learningmodel may include a probability that the image includes a layeringdefect. In one embodiment, the output includes, for each type of defectthat the machine learning model has been trained to detect, theprobability that a defect of that type is included in the image. Inanother embodiment, the output of the machine learning model may includea defect rating indicating the severity of a defect identified in theimage. In another embodiment, the output of the machine learning modelmay include an identification of a location within the image where alayering defect was identified. The output may further include ahighlight of the location of the defect in the image.

The machine learning model may be composed of a single level of linearor non-linear operations (e.g., a support vector machine (SVM) or asingle level neural network) or may be a deep neural network that iscomposed of multiple levels of non-linear operations. Examples of deepnetworks and neural networks include convolutional neural networksand/or recurrent neural networks with one or more hidden layers. Someneural networks may be composed of interconnected nodes, where each nodereceives input from a previous node, performs one or more operations,and sends the resultant output to one or more other connected nodes forfuture processing.

Convolutional neural networks include architectures that may provideefficient image recognition. Convolutional neural networks may includeseveral convolutional layers and subsampling layers that apply filtersto portions of the image of the text to detect certain features (e.g.,defects). That is, a convolutional neural network includes a convolutionoperation, which multiplies each image fragment by filters (e.g.,matrices) element-by-element and sums the results in a similar positionin an output image.

Recurrent neural networks may propagate data forwards, and alsobackwards, from later processing stages to earlier processing stages.Recurrent neural networks include functionality to process informationsequences and store information about previous computations in thecontext of a hidden layer. As such, recurrent neural networks may have a“memory”.

Artificial neural networks generally include a feature representationcomponent with a classifier or regression layers that map features to adesired output space. A convolutional neural network (CNN), for example,hosts multiple layers of convolutional filters. Pooling is performed,and non-linearities may be addressed, at lower layers, on top of which amulti-layer perceptron is commonly appended, mapping top layer featuresextracted by the convolutional layers to decisions (e.g. classificationoutputs). Deep learning is a class of machine learning algorithms thatuse a cascade of multiple layers of nonlinear processing units forfeature extraction and transformation. Each successive layer uses theoutput from the previous layer as input. Deep neural networks may learnin a supervised (e.g., classification) and/or unsupervised (e.g.,pattern analysis) manner. Deep neural networks include a hierarchy oflayers, where the different layers learn different levels ofrepresentations that correspond to different levels of abstraction. Indeep learning, each level learns to transform its input data into aslightly more abstract and composite representation. In an imagerecognition application, for example, the raw input may be a matrix ofpixels; the first representational layer may abstract the pixels andencode edges; the second layer may compose and encode arrangements ofedges; the third layer may encode higher level shapes (e.g., teeth,lips, gums, etc.); and the fourth layer may recognize that the imagecontains a face or define a bounding box around teeth in the image.Notably, a deep learning process can learn which features to optimallyplace in which level on its own. The “deep” in “deep learning” refers tothe number of layers through which the data is transformed. Moreprecisely, deep learning systems have a substantial credit assignmentpath (CAP) depth. The CAP is the chain of transformations from input tooutput. CAPs describe potentially causal connections between input andoutput. For a feedforward neural network, the depth of the CAPs may bethat of the network and may be the number of hidden layers plus one. Forrecurrent neural networks, in which a signal may propagate through alayer more than once, the CAP depth is potentially unlimited.

The machine learning model that identifies defects from images of 3Dprinted objects may be trained using a training dataset. Training of aneural network may be achieved in a supervised learning manner, whichinvolves feeding a training dataset consisting of labeled inputs throughthe network, observing its outputs, defining an error (by measuring thedifference between the outputs and the label values), and usingtechniques such as deep gradient descent and backpropagation to tune theweights of the network across all its layers and nodes such that theerror is minimized. In many applications, repeating this process acrossthe many labeled inputs in the training dataset yields a network thatcan produce correct output when presented with inputs that are differentthan the ones present in the training dataset. In high-dimensionalsettings, such as large images, this generalization is achieved when asufficiently large and diverse training dataset is made available. Thetraining dataset may include many images of 3D printed objects. Eachimage may include a label or target for that image. The label or targetmay indicate whether the image includes a defect, a type of defect, alocation of one or more defects, a severity of defect, and/or otherinformation.

In one embodiment, training of the machine learning model is ongoing.Accordingly, as new images are generated, the machine learning model maybe applied to identify defects in those images. In some instances a partmay be based on the output of the machine learning model, but the partmay ultimately fail due to undetected defects. This information may beadded to the images that were processed, and those images may be fedback through the machine learning model in an updated learning processto further teach the machine learning model and reduce future falsenegatives.

At block 620, it is determined by the processing logic whether the 3Dprinted object includes a layering defect, and/or whether the 3D printedobject includes one or more layering defects that will adversely affectuse of the 3D printed object for its intended purpose. The processinglogic may evaluate the machine learning model output for the image, aswell as all other images of the plurality of images in accordance withmethods described above. In one embodiment, the output generated by themachine learning model may include the defect rating for each defectidentified in each image of the plurality of images. The processinglogic may compare the output of the machine learning model (e.g., thedefect ratings) to a defect threshold. In one embodiment, the processinglogic may compare the output for each image to the defect threshold. Inanother embodiment, the processing logic may generate an overallcombined defect rating for the plurality of images and compare theoverall combined defect rating to the defect threshold. If the output isabove the defect threshold, the processing logic may determine that alayering defect is identified in the images associated with the 3Dprinted object, and the method 600 may continue to block 622. If theoutput is below the defect threshold, the processing logic may determinethat a manufacturing defect is not identified in the 3D printed object,and the method 600 may terminate.

If it is determined by the processing logic that the 3D printed objectincludes a manufacturing defect, at block 622, the 3D printed object maybe discarded. In one embodiment, the 3D printed object may be fixed toremove the layering defect. In another embodiment, the 3D printed objectmay be scrapped and a replacement 3D printed object may be manufactured.

FIG. 7 illustrates a 3D printed object 700, including a plurality oflines 702 formed as a result of a 3D printing process, in accordancewith one embodiment. In one embodiment, the 3D printed object maycomprise a mold of a dental arch used to fabricate an orthodontic and/orpolymeric aligner. In some instances, SLA is used to fabricate the mold.As a result of the SLA process, a 3D printed object 700 may include aplurality of lines 702 indicating the thin layers formed during the SLAprocess.

FIG. 8A illustrates an image of a 3D printed object generated by animaging system depicted in FIG. 1A, 1B, 2A or 2B, in accordance with oneembodiment. The image may depict a distinct region 802 of the 3D printedobject. In one embodiment, the 3D printed object may depict a mold foran orthodontic or polymeric aligner, as seen in one embodiment of FIG.7, and the distinct region 802 may include a tooth of the mold. Thedistinct region 802 may include a plurality of thin layers formed by alayer-by-layer 3D printing process (e.g., SLA).

FIG. 8B illustrates an exploded view of the distinct region 802 from the3D printed object of FIG. 8A. The distinct region 802 includes aplurality of thin layers 804 formed during a layer-by-layer 3D printingprocess (e.g., SLA). The space between the thin layers 804 is depictedas a plurality of lines 806. FIG. 8B illustrates an ideal view of a 3Dprinted object fabricated by a layer-by-layer SLA process. The thinlayers 804 do not contain any layering defects caused by a manufacturingmalfunction. Additionally, the lines 806 are spaced evenly apart,indicating that the thin layers 804 have an equal thickness.

FIG. 8C illustrates another exploded view of the distinct region 802 forthe 3D printed object of FIG. 8A. FIG. 8C depicts a plurality oflayering defects 808 in the form of holes (e.g., pits) formed on thesurface to interior interface of the mold.

FIG. 9 illustrates an example user interface (UI) 900 for system controland image acquisition (e.g., for software and/or firmware comprisingimage inspection module 145 and/or imager control module 140 of FIG.1A), in accordance with one embodiment. The UI 900 may be interactive,and user interactive options may be represented as icons. The user mayinteract with the UI by various input (e.g., mouse, keyboard,touchscreen, or other similar devices).

An overhead view area 902 may be provided in one section of UI 900. Theoverhead view area 902 may be configured to display an image of a 3Dprinted object. The image of the 3D printed object may be generated by acamera of the imaging system depicted in FIGS. 1A, 1B, 2A and/or 2B. Theimage may be generated by a top view camera depicted in FIG. 1A, 2A or2B, for example. The image displayed in the overhead view area 902 maydepict a top view image of the 3D printed object captured by the topview camera.

An inspection view area 904 may be provided in one section of the UI900. The inspection view area 904 may be configured to display adifferent image of the 3D printed object than the image displayed in theoverhead view area 902. The image displayed in the inspection view area904 may be generated by a side view camera of the imaging systemdepicted in FIGS. 1A, 1B, 2A and/or 2B. In one embodiment, the side viewcamera may be configured to acquire side view images of the 3D printedobject. The image displayed in the inspection view area 904 may depict aside view image captured by the side view camera. The image may depict adistinct region of the 3D printed object and may depict the thin layersformed by a layer-by-layer 3D printing process (e.g., SLA). In oneembodiment, the UI 900 may include a zoom fit 918 icon, which may allowa user to automatically zoom in on the image displayed in the inspectionview area 904 and may fit the zoomed in portion of the image to thebounds of the inspection view area 904.

An image menu 906 may be provided in one section of the UI 900. Eachentry of the image menu 906 may include an image depicting a distinctregion of the 3D printed object that is depicted in the images displayedin the overhead view area 902 and the inspection view area 904. Eachentry may further include a fail indicator which indicates whether thedistinct region depicted in the corresponding image contains one or moredefects and subsequently fails inspection. If a user selects an entry ofthe image menu 906, the image associated with the entry may be displayedin the inspection view area 904. In one embodiment, the image displayedin the inspection view area 904 may further include a visual indicatorthat identifies for the user the exact location of the defect on thedistinct region of the 3D printed object.

A result area 908 may be provided in one section of the UI 900. Theresult area 908 may display output results generated from a machinelearning model and/or a set of rules applied by a rules engine. Theresults area 908 may indicate whether an image of the 3D printed objecthas been generated and has been successfully processed by the machinelearning model. In one embodiment, the overhead view area 902 mayinclude an image depicting a top view of the 3D printed object. In afurther embodiment, the result area 908 may indicate whether an IDassociated with the 3D printed object was successfully identified. Theresults area 908 may further indicate whether a gross defect is presenton the 3D printed object. If a gross defect is present on the 3D printedobject, results area 908 may indicate that a profile of the object failsinspection. The results area 908 may further indicate whether a layeringdefect is present in the 3D printed object. If a layering defect ispresent in the 3D printed object, the results area 908 may indicate thatthe 3D printed object fails inspection.

In one embodiment, UI 900 may include a fail indicator 910. The failindicator 910 may indicate to a user that the 3D printed object hasfailed inspection by containing a manufacturing defect that will degradeperformance of the 3D printed object (e.g., a gross defect a layeringdefect, etc.). The fail indicator 910 may indicate that the 3D printedobject has failed inspection based on the results provided in theresults area 908. In one embodiment, the fail indicator 910 may includean icon wherein a user (e.g., a technician) selecting the icon maymanually fail the inspection of the 3D printed object based on theimages displayed in the overhead view area 902 and the inspection viewarea 904.

A statistics area 912 may be provided in one section of the UI 900. Thestatistics area 912 may provide statistics based on the total number of3D printed objects tested in a given set of 3D printed objects. Thestatistics area 912 may indicate a number of images of a 3D printedobject that have passed inspection and a number of images of the 3Dprinted object that have failed inspection. The statistics area 912 mayfurther include the yield of images tested for the given 3D printedobject. The UI 900 may further include a statistics reset button 914. Auser (e.g., a technician) may reset the statistics area 912 such thatthe total number of images tested for a given set of images is set tozero.

An ID entry 916 may be provided in one section of the UI 900. In oneembodiment, a user (e.g., a technician) may manually enter an ID (e.g.,a serial number) associated with the 3D printed object depicted in theimages displayed in the overhead view area 902 and the inspection viewarea 904 into the ID entry 916. In another embodiment, the ID may beautomatically populated into the ID entry 916 by the processing logic ofthe imaging system depicted in FIGS. 1A, 1B, 2A and/or 2B. In oneembodiment, OCR may be performed on the image displayed in the overheadview area 902 to identify a symbol sequence in the image, and theprocessing logic may generate an ID associated with the 3D printedobject. The ID may be obtained based on a known order of the 3D printedobject in an object sorting system, as described in embodiments herein.

Icons for a manual mode 920 and an automatic mode 922 may be provided inone section of the UI 900. In one embodiment, the manual mode 920 iconmay be selected by a user (e.g., a technician). If the manual mode 920icon is selected, the user may manually inspect the 3D printed objectdepicted in the images displayed in the overhead view area 902 and theinspection view area 904. The fail indicator 910 may include a buttonwherein the user may manually fail the inspection of the 3D printedobject based on the images. In another embodiment, the automatic mode922 icon may be selected. If the automatic mode 922 icon is selected,the processing logic may cause the machine learning logic to process theimages displayed in the overhead view area 902 and the inspection viewarea 904 so to determine whether a manufacturing defect is present.

An icon for image capture 924 may be provided in one section of the UI900. In one embodiment, a user (e.g., a technician) may manually captureimages of the 3D printed object by selecting the image capture 924 icon.In another embodiment, the processing logic may cause at least one imageof the 3D printed object to be captured in accordance to methodsdisclosed herein.

Icons for settings 928, diagnostics 930, emergency stop 932 and exit 934may be provided in one section of the UI 900. In one embodiment, a user(e.g., a technician) may select the diagnostics 930 icon to modify theparameters of the imaging system depicted in FIGS. 1A, 1B, 2A and/or 2B,in accordance with UI 1000 depicted in FIG. 10. In one embodiment, theuser may stop the imaging system from capturing images of the 3D printedobject by selecting the emergency stop 932 icon. The user may modify thesettings of the program displaying the UI 900 by selecting the settings928 icon. The user may exit the program displaying the UI 900 byselecting the exit 934 icon.

FIG. 10 illustrates an example UI 1000 for engineering control for theimaging system depicted in FIGS. 1A, 1B, 2A and/or 2B, in accordancewith one embodiment. UI 1000 may be displayed to a user (e.g., atechnician) if the user selects the diagnostics 930 icon depicted inFIG. 9. The UI 1000 may be interactive, and user interactive options maybe represented as icons. The user may interact with the UI by variousinput (e.g., mouse, keyboard, touchscreen, or other similar devices). Inone embodiment, the UI 1000 is a UI of the imager control module 140 ofFIG. 1A.

Icons for a motion control menu 1002, camera/lighting menu 1004, I/Omenu 1006, and a miscellaneous menu 1008 may be provided on the UI 1000.A user (e.g., a technician) may adjust various parameters associatedwith different components of the imaging system depicted in FIGS. 1A and1B by selecting an icon associated with a component. For example, theuser may adjust the parameters regarding the motion control of themulti-axis platform of the platform apparatus, or the moveable base ofthe side view camera apparatus depicted in FIGS. 1A and 1B by selectingthe motion control menu 1002 icon. The motion control menu 1002 ishighlighted in FIG. 10.

A rotary control area 1010 may be provided in one section of the UI1000. The rotary control area 1010 may allow the user to modify theparameters associated with the platform of the platform apparatus. Inone embodiment, the user may modify the degree of rotation of theplatform by entering a numerical value into a degree entry field 1016.In a further embodiment, the user may modify the degree of rotation ofthe platform absolutely (e.g., to select a particular rotation positionsetting) by selecting a move absolute 1012 icon. In another embodiment,the user may modify the degree of rotation of the platform relative to aprevious degree of rotation by selecting a move relative 1014 icon. Inone embodiment, the user may modify the velocity of the platformrotation and/or translational motion by entering a numerical value intoa velocity entry field 1020 and selecting a set velocity 1018 icon. Inanother embodiment, the user may modify the acceleration of the platformrotation and/or translational motion by entering a numerical value intoan acceleration entry field 1024 and selecting a setacceleration/deceleration 1022 icon. If the user enters a positivenumerical value into the acceleration entry field 1024, the platform mayincrease acceleration. If the user enters a negative numerical valueinto the acceleration entry field 1024, the platform may decreaseacceleration. In one embodiment, the user may stop the rotation and/ortranslational motion of the platform by selecting a stop motion 1025icon.

Icons to return home 1026, enable drive 1028, or disable drive 1030 maybe included in the rotary control area 1010. A user may cause theplatform to return to a “home” setting by selecting the home 1026 icon.The “home” setting may include a set of parameters for the base that areapplied when each 3D printed object is first processed. A user mayenable drive of the platform (e.g., to begin rotation and/ortranslational motion) by selecting the enable drive 1028 icon. A usermay disable drive of the (e.g., to stop rotation and/or translationalmotion) by selecting the disable drive 1030 icon.

The rotary control area 1010 may also include a rotary status area 1032.The rotary status area 1032 may indicate the current parameters and thestatus of the platform. In one embodiment, the rotary status area 1032may indicate whether drive is enabled, the platform is moving, a programis executing, or the platform is set to a “home” setting. In a furtherembodiment, the rotary status area 1032 may provide the current degreeposition of the platform and the current velocity of which the platformis rotating. In one embodiment, if an error has occurred in operationplatform, an error code may be provided in the rotary status area 1032.

A linear control area 1034 may be provided in one section of the UI1000. The linear control area 1034 may allow a user (e.g., a technician)to modify the parameters associated with the moveable base of the sideview camera apparatus depicted in FIGS. 1A and 1B. In one embodiment,the user may modify the location of the moveable base by entering anumerical value into a location entry field 1040. In a furtherembodiment, the user may modify the location of the moveable baseabsolutely by selecting a move absolute 1036 icon. In anotherembodiment, the user may modify the location of the moveable baserelative to a previous location by selecting a move relative 1038 icon.In one embodiment, the user may modify the velocity the moveable basemoves by entering a numerical value into a velocity entry field 1044 andselecting the set velocity 1042 icon. In another embodiment, the usermay modify the acceleration of the moveable base by entering a numericalvalue into an acceleration entry field 1048 and selecting a setacceleration/deceleration 1049 icon. If the user enters a positivenumerical value into the acceleration entry field 1024, the moveablebase may increase acceleration. If the user enters a negative numericalvalue into the acceleration entry field 1024, the moveable base maydecrease acceleration.

Icons to return home 1050, enable drive 1052, or disable drive 1054 maybe included in the linear control area 1034. A user may cause themoveable base to return to a “home” setting by selecting the home 1050icon. The “home” setting may include a set of parameters for themoveable base that are applied when each 3D printed object is firstprocessed. A user may enable drive of the moveable base by selecting theenable drive 1052 icon. A user may disable drive of the moveable base byselecting the disable drive 1058 icon.

The linear control area 1034 may also include a linear status area 1056.The linear status area 1056 may indicate the current parameters and thestatus of the moveable base. In one embodiment, the linear status area1056 may indicate whether a drive is enabled, the moveable base ismoving, or the moveable base is set to a “home” setting. In a furtherembodiment, the rotary status area 1032 may provide the current locationof the moveable base and the current velocity of which the moveable baseis moving. In one embodiment, if an error has occurred in operation ofthe moveable base, an error code may be provided in the linear statusarea 1056.

A coordinated control area 1060 may be provided in one section of the UI1000. The coordinated control area 1060 may allow a user (e.g., atechnician) to cause the processing logic to automatically modify theparameters associated with the platform of the platform apparatus, andthe moveable base of the side view camera apparatus depicted in FIGS. 1Aand 1B (e.g., according to some inspection recipe). A user may cause theprocessing logic to move the start position of the platform of theplatform apparatus and/or the moveable base of the side view cameraapparatus by selecting a move start position 1062 icon. A user may causethe processing logic to determine a motion profile for the 3D printedobject under inspection by selecting a run profile 1064 icon. A motionprofile may be determined by the processing logic in accordance with oneor more properties associated with the 3D printed object. The one ormore properties may be stored in a digital file associated with the 3Dprinted object. In one embodiment, a user may select a previouslygenerated motion profile from a plurality of motion profiles from aprofile drop down menu 1066.

After a user has modified at least one parameter in one or more of therotary control area 1010, the linear control area 1034, and/or thecoordinated control area, the user may apply the modified parameters tothe imaging system by selecting a done 1068 icon. The UI 900 depicted inFIG. 9 may be presented to the user upon selection of the done 1068icon.

FIG. 11 illustrates a diagrammatic representation of a machine in theexample form of a computing device 1100 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed with reference to the methods of FIGS. 3A-6. Inalternative embodiments, the machine may be connected (e.g., networked)to other machines in a Local Area Network (LAN), an intranet, anextranet, or the Internet. For example, the machine may be networked toa rapid prototyping apparatus such as a 3D printer or SLA apparatus. Inanother example, the machine may be networked to or directly connectedto an imaging system (e.g., imaging system 101 of FIGS. 1A, 1B, 2Aand/or 2B. In one embodiment, the computing device 1100 corresponds tothe computing device 135 of FIG. 1A. The machine may operate in thecapacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet computer, a set-top box (STB), a Personal Digital Assistant(PDA), a cellular telephone, a web appliance, a server, a networkrouter, a switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” shall also be taken to include any collection ofmachines (e.g., computers) that individually or jointly execute a set(or multiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computing device 1100 includes a processing device 1102, amain memory 1104 (e.g., read only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), astatic memory 1106 (e.g., flash memory, static random access memory(SRAM), etc.), and a secondary memory (e.g., a data storage device1128), which communicate with each other via a bus 1108.

Processing device 1102 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 1102 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 1102may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 1102 is configured to execute theprocessing logic (instructions 1126) for performing operations and stepsdiscussed herein.

The computing device 1100 may further include a network interface device1122 for communicating with a network 1164. The computing device 1100also may include a video display unit 1110 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1112 (e.g., a keyboard), a cursor control device 1114 (e.g., a mouse),and a signal generation device 1120 (e.g., a speaker).

The data storage device 1128 may include a machine-readable storagemedium (or more specifically a non-transitory computer-readable storagemedium) 1124 on which is stored one or more sets of instructions 1126embodying any one or more of the methodologies or functions describedherein. A non-transitory storage medium refers to a storage medium otherthan a carrier wave. The instructions 1126 may also reside, completelyor at least partially, within the main memory 1104 and/or within theprocessing device 1102 during execution thereof by the computer device1100, the main memory 1104 and the processing device 1102 alsoconstituting computer-readable storage media.

The computer-readable storage medium 1124 may also be used to an imageinspection module 145 and/or imager control module 140 as describedherein above, which may perform one or more of the operations of methodsdescribed with reference to FIGS. 3A-6. The computer readable storagemedium 1124 may also store a software library containing methods thatcall an image inspection module 145 and/or imager control module 140.While the computer-readable storage medium 1124 is shown in an exampleembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, and other non-transitory computer-readable media.

As discussed herein above, in some embodiments, the defect detectionsystems of FIGS. 1A, 2A and 2B and methods 300-600 of FIGS. 3A-6 may beused to perform automated defect detection of molds of dental archesused to manufacture aligners and/or to perform automated defectdetection of directly printed aligners. FIG. 12A illustrates anexemplary tooth repositioning appliance or aligner 1200 that can be wornby a patient in order to achieve an incremental repositioning ofindividual teeth 1202 in the jaw. The appliance can include a shell(e.g., a continuous polymeric shell or a segmented shell) havingteeth-receiving cavities that receive and resiliently reposition theteeth. An aligner (also referred to as an appliance) or portion(s)thereof may be indirectly fabricated using a physical model of teeth.For example, an appliance (e.g., polymeric appliance) can be formedusing a physical model of teeth and a sheet of suitable layers ofpolymeric material. A “polymeric material,” as used herein, may includeany material formed from a polymer. A “polymer,” as used herein, mayrefer to a molecule composed of repeating structural units connected bycovalent chemical bonds often characterized by a substantial number ofrepeating units (e.g., equal or greater than 3 repeating units,optionally, in some embodiments equal to or greater than 10 repeatingunits, in some embodiments greater or equal to 30 repeating units) and ahigh molecular weight (e.g., greater than or equal to 10,000 Da, in someembodiments greater than or equal to 50,000 Da or greater than or equalto 100,000 Da). Polymers are commonly the polymerization product of oneor more monomer precursors. The term polymer includes homopolymers, orpolymers consisting essentially of a single repeating monomer subunit.The term polymer also includes copolymers which are formed when two ormore different types of monomers are linked in the same polymer. Usefulpolymers include organic polymers or inorganic polymers that may be inamorphous, semi-amorphous, crystalline or semi-crystalline states.Polymers may include polyolefins, polyesters, polyacrylates,polymethacrylates, polystyrenes, polypropylenes, polyethylenes,polyethylene terephthalates, poly lactic acid, polyurethanes, epoxidepolymers, polyethers, poly(vinyl chlorides), polysiloxanes,polycarbonates, polyamides, poly acrylonitriles, polybutadienes,poly(cycloolefins), and copolymers. The systems and/or methods providedherein are compatible with a range of plastics and/or polymers.Accordingly, this list is not inclusive, but rather is exemplary. Theplastics can be thermosets or thermoplastics. The plastic may bethermoplastic.

Examples of materials applicable to the embodiments disclosed hereininclude, but are not limited to, those materials described in thefollowing Provisional patent applications filed by Align Technology:“MULTIMATERIAL ALIGNERS,” U.S. Prov. App. Ser. No. 62/189,259, filedJul. 7, 2015; “DIRECT FABRICATION OF ALIGNERS WITH INTERPROXIMAL FORCECOUPLING”, U.S. Prov. App. Ser. No. 62/189,263, filed Jul. 7, 2015;“DIRECT FABRICATION OF ORTHODONTIC APPLIANCES WITH VARIABLE PROPERTIES,”U.S. Prov. App. Ser. No. 62/189,291, filed Jul. 7, 2015; “DIRECTFABRICATION OF ALIGNERS FOR ARCH EXPANSION”, U.S. Prov. App. Ser. No.62/189,271, filed Jul. 7, 2015; “DIRECT FABRICATION OF ATTACHMENTTEMPLATES WITH ADHESIVE,” U.S. Prov. App. Ser. No. 62/189,282, filedJul. 7, 2015; “DIRECT FABRICATION CROSS-LINKING FOR PALATE EXPANSION ANDOTHER APPLICATIONS”, U.S. Prov. App. Ser. No. 62/189,301, filed Jul. 7,2015; “SYSTEMS, APPARATUSES AND METHODS FOR DENTAL APPLIANCES WITHINTEGRALLY FORMED FEATURES”, U.S. Prov. App. Ser. No. 62/189,312, filedJul. 7, 2015; “DIRECT FABRICATION OF POWER ARMS”, U.S. Prov. App. Ser.No. 62/189,317, filed Jul. 7, 2015; “SYSTEMS, APPARATUSES AND METHODSFOR DRUG DELIVERY FROM DENTAL APPLIANCES WITH INTEGRALLY FORMEDRESERVOIRS”, U.S. Prov. App. Ser. No. 62/189,303, filed Jul. 7, 2015;“DENTAL APPLIANCE HAVING ORNAMENTAL DESIGN”, U.S. Prov. App. Ser. No.62/189,318, filed Jul. 7, 2015; “DENTAL MATERIALS USING THERMOSETPOLYMERS,” U.S. Prov. App. Ser. No. 62/189,380, filed Jul. 7, 2015;“CURABLE COMPOSITION FOR USE IN A HIGH TEMPERATURE LITHOGRAPHY-BASEDPHOTOPOLYMERIZATION PROCESS AND METHOD OF PRODUCING CROSSLINKED POLYMERSTHEREFROM,” U.S. Prov. App. Ser. No. 62/667,354, filed May 4, 2018;“POLYMERIZABLE MONOMERS AND METHOD OF POLYMERIZING THE SAME,” U.S. Prov.App. Ser. No. 62/667,364, filed May 4, 2018; and any conversionapplications thereof (including publications and issued patents),including any divisional, continuation, or continuation-in-part thereof.

The appliance 1200 can fit over all teeth present in an upper or lowerjaw, or less than all of the teeth. The appliance can be designedspecifically to accommodate the teeth of the patient (e.g., thetopography of the tooth-receiving cavities matches the topography of thepatient's teeth), and may be fabricated based on positive or negativemodels of the patient's teeth generated by impression, scanning, and thelike. Alternatively, the appliance can be a generic appliance configuredto receive the teeth, but not necessarily shaped to match the topographyof the patient's teeth. In some cases, only certain teeth received by anappliance will be repositioned by the appliance while other teeth canprovide a base or anchor region for holding the appliance in place as itapplies force against the tooth or teeth targeted for repositioning. Insome cases, some, most, or even all of the teeth will be repositioned atsome point during treatment. Teeth that are moved an also serve as abase or anchor for holding the appliance in place over the teeth. Insome cases, however, it may be desirable or necessary to provideindividual attachments or other anchoring elements 1204 on teeth 1202with corresponding receptacles or apertures 1206 in the appliance 1200so that the appliance can apply a selected force on the tooth. Exemplaryappliances, including those utilized in the Invisalign® System, aredescribed in numerous patents and patent applications assigned to AlignTechnology, Inc. including, for example, in U.S. Pat. Nos. 6,450,807,and 5,975,893, as well as on the company's website, which is accessibleon the World Wide Web (see, e.g., the URL “invisalign.com”). Examples oftooth-mounted attachments suitable for use with orthodontic appliancesare also described in patents and patent applications assigned to AlignTechnology, Inc., including, for example, U.S. Pat. Nos. 6,309,215 and6,830,450.

FIG. 12B illustrates a tooth repositioning system 1210 including aplurality of appliances 1212, 1214, and 1216. Any of the appliancesdescribed herein can be designed and/or provided as part of a set of aplurality of appliances used in a tooth repositioning system. Eachappliance may be configured so a tooth-receiving cavity has a geometrycorresponding to an intermediate or final tooth arrangement intended forthe appliance. The patient's teeth can be progressively repositionedfrom an initial tooth arrangement to a target tooth arrangement byplacing a series of incremental position adjustment appliances over thepatient's teeth. For example, the tooth repositioning system 1210 caninclude a first appliance 1212 corresponding to an initial tootharrangement, one or more intermediate appliances 1214 corresponding toone or more intermediate arrangements, and a final appliance 1216corresponding to a target arrangement. A target tooth arrangement can bea planned final tooth arrangement selected for the patient's teeth atthe end of all planned orthodontic treatment. Alternatively, a targetarrangement can be one of some intermediate arrangements for thepatient's teeth during the course of orthodontic treatment, which mayinclude various different treatment scenarios, including, but notlimited to, instances where surgery is recommended, where interproximalreduction (IPR) is appropriate, where a progress check is scheduled,where anchor placement is best, where palatal expansion is desirable,where restorative dentistry is involved (e.g., inlays, onlays, crowns,bridges, implants, veneers, and the like), etc. As such, it isunderstood that a target tooth arrangement can be any planned resultingarrangement for the patient's teeth that follows one or more incrementalrepositioning stages. Likewise, an initial tooth arrangement can be anyinitial arrangement for the patient's teeth that is followed by one ormore incremental repositioning stages.

In some embodiments, the appliances 1212, 1214, 1216, or portionsthereof, can be produced using indirect fabrication techniques, such asthermoforming over a positive or negative mold, which may be inspectedusing the methods and systems described herein above. Indirectfabrication of an orthodontic appliance can involve producing a positiveor negative mold of the patient's dentition in a target arrangement(e.g., by rapid prototyping, milling, etc.) and thermoforming one ormore sheets of material over the mold in order to generate an applianceshell.

In an example of indirect fabrication, a mold of a patient's dental archmay be fabricated from a digital model of the dental arch, and a shellmay be formed over the mold (e.g., by thermoforming a polymeric sheetover the mold of the dental arch and then trimming the thermoformedpolymeric sheet). The fabrication of the mold may be formed by a rapidprototyping machine (e.g., a SLA 3D printer). The rapid prototypingmachine may receive digital models of molds of dental arches and/ordigital models of the appliances 1212, 1214, 1216 after the digitalmodels of the appliances 1212, 1214, 1216 have been processed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programming logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof.

To manufacture the molds, a shape of a dental arch for a patient at atreatment stage is determined based on a treatment plan. In the exampleof orthodontics, the treatment plan may be generated based on anintraoral scan of a dental arch to be molded. The intraoral scan of thepatient's dental arch may be performed to generate a three dimensional(3D) virtual model of the patient's dental arch (mold). For example, afull scan of the mandibular and/or maxillary arches of a patient may beperformed to generate 3D virtual models thereof. The intraoral scan maybe performed by creating multiple overlapping intraoral images fromdifferent scanning stations and then stitching together the intraoralimages to provide a composite 3D virtual model. In other applications,virtual 3D models may also be generated based on scans of an object tobe modeled or based on use of computer aided drafting technologies(e.g., to design the virtual 3D mold). Alternatively, an initialnegative mold may be generated from an actual to be modeled (e.g., adental impression or the like). The negative mold may then be scanned todetermine a shape of a positive mold that will be produced.

Once the virtual 3D model of the patient's dental arch is generated, adental practitioner may determine a desired treatment outcome, whichincludes final positions and orientations for the patient's teeth.Processing logic may then determine a number of treatment stages tocause the teeth to progress from starting positions and orientations tothe target final positions and orientations. The shape of the finalvirtual 3D model and each intermediate virtual 3D model may bedetermined by computing the progression of tooth movement throughoutorthodontic treatment from initial tooth placement and orientation tofinal corrected tooth placement and orientation. For each treatmentstage, a separate virtual 3D model will be different. The originalvirtual 3D model, the final virtual model 3D model and each intermediatevirtual 3D model is unique and customized to the patient.

Accordingly, multiple different virtual 3D models (digital designs) of adental arch may be generated for a single patient. A first virtual 3Dmodel may be a unique model of a patient's dental arch and/or teeth asthey presently exist, and a final virtual 3D may be a model of thepatient's dental arch and/or teeth after correction of one or more teethand/or a jaw. Multiple intermediate virtual 3D models may be modeled,each of which may be incrementally different from previous virtual 3Dmodels.

Each virtual 3D model of a patient's dental arch may be used to generatecustomized physical mold of the dental arch at a particular stage oftreatment. The shape of the mold may be at least in part based on theshape of the virtual 3D model for that treatment stage. The virtual 3Dmodel may be represented in a file such as a computer aided drafting(CAD) file or a 3D printable file such as a stereolithography (STL)file. The virtual 3D model for the mold may be sent to a third party(e.g., clinician office, laboratory, manufacturing facility or otherentity). The virtual 3D model may include instructions that will controla fabrication system or device in order to produce the mold withspecific geometries.

A clinician office, laboratory, manufacturing facility or other entitymay receive the virtual 3D model of the mold, the digital model havingbeen created as set forth above. The entity may input the digital modelinto a rapid prototyping machine. The rapid prototyping machine thenmanufactures the mold using the digital model. One example of a rapidprototyping manufacturing machine is a 3D printer. 3D printing includesany layer-based additive manufacturing processes. 3D printing may beachieved using an additive process, where successive layers of materialare formed in proscribed shapes. 3D printing may be performed usingextrusion deposition, granular materials binding, lamination,photopolymerization, continuous liquid interface production (CLIP), orother techniques. 3D printing may also be achieved using a subtractiveprocess, such as milling.

In some instances SLA is used to fabricate an SLA mold. In SLA, the moldis fabricated by successively printing thin layers of a photo-curablematerial (e.g., a polymeric resin) on top of one another. A platformrests in a bath of liquid photopolymer or resin just below a surface ofthe bath. A light source (e.g., an ultraviolet laser) traces a patternover the platform, curing the photopolymer where the light source isdirected, to form a first layer of the mold. The platform is loweredincrementally, and the light source traces a new pattern over theplatform to form another layer of the mold at each increment. Thisprocess repeats until the mold is completely fabricated. Once all of thelayers of the mold are formed, the mold may be cleaned and cured.

Materials such as polyester, a co-polyester, a polycarbonate, athermopolymeric polyurethane, a polypropylene, a polyethylene, apolypropylene and polyethylene copolymer, an acrylic, a cyclic blockcopolymer, a polyetheretherketone, a polyamide, a polyethyleneterephthalate, a polybutylene terephthalate, a polyetherimide, apolyethersulfone, a polytrimethylene terephthalate, a styrenic blockcopolymer (SBC), a silicone rubber, an elastomeric alloy, athermopolymeric elastomer (TPE), a thermopolymeric vulcanizate (TPV)elastomer, a polyurethane elastomer, a block copolymer elastomer, apolyolefin blend elastomer, a thermopolymeric co-polyester elastomer, athermopolymeric polyamide elastomer, or combinations thereof, may beused to directly form the mold. The materials used for fabrication ofthe mold can be provided in an uncured form (e.g., as a liquid, resin,powder, etc.) and can be cured (e.g., by photopolymerization, lightcuring, gas curing, laser curing, crosslinking, etc.). The properties ofthe material before curing may differ from the properties of thematerial after curing.

After the mold is generated, it may be inspected using the systemsand/or methods described herein above. If the mold passes theinspection, then it may be used to form an appliance (e.g., an aligner).

Appliances may be formed from each mold and when applied to the teeth ofthe patient, may provide forces to move the patient's teeth as dictatedby the treatment plan. The shape of each appliance is unique andcustomized for a particular patient and a particular treatment stage. Inan example, the appliances 1212, 1214, and 1216 can be pressure formedor thermoformed over the molds. Each mold may be used to fabricate anappliance that will apply forces to the patient's teeth at a particularstage of the orthodontic treatment. The appliances 1212, 1214, and 1216each have teeth-receiving cavities that receive and resilientlyreposition the teeth in accordance with a particular treatment stage.

In one embodiment, a sheet of material is pressure formed orthermoformed over the mold. The sheet may be, for example, a sheet ofpolymeric (e.g., an elastic thermopolymeric, a sheet of polymericmaterial, etc.). To thermoform the shell over the mold, the sheet ofmaterial may be heated to a temperature at which the sheet becomespliable. Pressure may concurrently be applied to the sheet to form thenow pliable sheet around the mold. Once the sheet cools, it will have ashape that conforms to the mold. In one embodiment, a release agent(e.g., a non-stick material) is applied to the mold before forming theshell. This may facilitate later removal of the mold from the shell.

Additional information may be added to the appliance. The additionalinformation may be any information that pertains to the aligner.Examples of such additional information includes a part numberidentifier, patient name, a patient identifier, a case number, asequence identifier (e.g., indicating which aligner a particular lineris in a treatment sequence), a date of manufacture, a clinician name, alogo and so forth. For example, after an appliance is thermoformed, thealigner may be laser marked with a part number identifier (e.g., serialnumber, barcode, or the like). In some embodiments, the system may beconfigured to read (e.g., optically, magnetically, or the like) anidentifier (barcode, serial number, electronic tag or the like) of themold to determine the part number associated with the aligner formedthereon. After determining the part number identifier, the system maythen tag the aligner with the unique part number identifier. The partnumber identifier may be computer readable and may associate thataligner to a specific patient, to a specific stage in the treatmentsequence, whether it is an upper or lower shell, a digital modelrepresenting the mold the aligner was manufactured from and/or a digitalfile including a virtually generated digital model or approximatedproperties thereof of that aligner (e.g., produced by approximating theouter surface of the aligner based on manipulating the digital model ofthe mold, inflating or scaling projections of the mold in differentplanes, etc.).

After an appliance is formed over a mold for a treatment stage, thatappliance is subsequently trimmed along a cutline (also referred to as atrim line) and the appliance may be removed from the mold. Theprocessing logic may determine a cutline for the appliance. Thedetermination of the cutline(s) may be made based on the virtual 3Dmodel of the dental arch at a particular treatment stage, based on avirtual 3D model of the appliance to be formed over the dental arch, ora combination of a virtual 3D model of the dental arch and a virtual 3Dmodel of the appliance. The location and shape of the cutline can beimportant to the functionality of the appliance (e.g., an ability of theappliance to apply desired forces to a patient's teeth) as well as thefit and comfort of the appliance. For shells such as orthodonticappliances, orthodontic retainers and orthodontic splints, the trimmingof the shell may play a role in the efficacy of the shell for itsintended purpose (e.g., aligning, retaining or positioning one or moreteeth of a patient) as well as the fit on a patient's dental arch. Forexample, if too much of the shell is trimmed, then the shell may loserigidity and an ability of the shell to exert force on a patient's teethmay be compromised. When too much of the shell is trimmed, the shell maybecome weaker at that location and may be a point of damage when apatient removes the shell from their teeth or when the shell is removedfrom the mold. In some embodiments, the cut line may be modified in thedigital design of the appliance as one of the corrective actions takenwhen a probable point of damage is determined to exist in the digitaldesign of the appliance.

On the other hand, if too little of the shell is trimmed, then portionsof the shell may impinge on a patient's gums and cause discomfort,swelling, and/or other dental issues. Additionally, if too little of theshell is trimmed at a location, then the shell may be too rigid at thatlocation. In some embodiments, the cutline may be a straight line acrossthe appliance at the gingival line, below the gingival line, or abovethe gingival line. In some embodiments, the cutline may be a gingivalcutline that represents an interface between an appliance and apatient's gingiva. In such embodiments, the cutline controls a distancebetween an edge of the appliance and a gum line or gingival surface of apatient.

Each patient has a unique dental arch with unique gingiva. Accordingly,the shape and position of the cutline may be unique and customized foreach patient and for each stage of treatment. For instance, the cutlineis customized to follow along the gum line (also referred to as thegingival line). In some embodiments, the cutline may be away from thegum line in some regions and on the gum line in other regions. Forexample, it may be desirable in some instances for the cutline to beaway from the gum line (e.g., not touching the gum) where the shell willtouch a tooth and on the gum line (e.g., touching the gum) in theinterproximal regions between teeth. Accordingly, it is important thatthe shell be trimmed along a predetermined cutline.

In some embodiments, the orthodontic appliances herein (or portionsthereof) can be produced using direct fabrication, such as additivemanufacturing techniques (also referred to herein as “3D printing) orsubtractive manufacturing techniques (e.g., milling). In someembodiments, direct fabrication involves forming an object (e.g., anorthodontic appliance or a portion thereof) without using a physicaltemplate (e.g., mold, mask etc.) to define the object geometry. Additivemanufacturing techniques can be categorized as follows: (1) vatphotopolymerization (e.g., stereolithography), in which an object isconstructed layer by layer from a vat of liquid photopolymer resin; (2)material jetting, in which material is jetted onto a build platformusing either a continuous or drop on demand (DOD) approach; (3) binderjetting, in which alternating layers of a build material (e.g., apowder-based material) and a binding material (e.g., a liquid binder)are deposited by a print head; (4) fused deposition modeling (FDM), inwhich material is drawn though a nozzle, heated, and deposited layer bylayer; (5) powder bed fusion, including but not limited to direct metallaser sintering (DMLS), electron beam melting (EBM), selective heatsintering (SHS), selective laser melting (SLM), and selective lasersintering (SLS); (6) sheet lamination, including but not limited tolaminated object manufacturing (LOM) and ultrasonic additivemanufacturing (UAM); and (7) directed energy deposition, including butnot limited to laser engineering net shaping, directed lightfabrication, direct metal deposition, and 3D laser cladding. Forexample, stereolithography can be used to directly fabricate one or moreof the appliances 1212, 1214, and 1216. In some embodiments,stereolithography involves selective polymerization of a photosensitiveresin (e.g., a photopolymer) according to a desired cross-sectionalshape using light (e.g., ultraviolet light). The object geometry can bebuilt up in a layer-by-layer fashion by sequentially polymerizing aplurality of object cross-sections. As another example, the appliances1212, 1214, and 1216 can be directly fabricated using selective lasersintering. In some embodiments, selective laser sintering involves usinga laser beam to selectively melt and fuse a layer of powdered materialaccording to a desired cross-sectional shape in order to build up theobject geometry. As yet another example, the appliances 1212, 1214, and1216 can be directly fabricated by fused deposition modeling. In someembodiments, fused deposition modeling involves melting and selectivelydepositing a thin filament of thermoplastic polymer in a layer-by-layermanner in order to form an object. In yet another example, materialjetting can be used to directly fabricate the appliances 1212, 1214, and1216. In some embodiments, material jetting involves jetting orextruding one or more materials onto a build surface in order to formsuccessive layers of the object geometry.

In some embodiments, the direct fabrication methods provided hereinbuild up the object geometry in a layer-by-layer fashion, withsuccessive layers being formed in discrete build steps. Alternatively orin combination, direct fabrication methods that allow for continuousbuild-up of an object geometry can be used, referred to herein as“continuous direct fabrication.” Various types of continuous directfabrication methods can be used. As an example, in some embodiments, theappliances 1212, 1214, and 1216 are fabricated using “continuous liquidinterphase printing,” in which an object is continuously built up from areservoir of photopolymerizable resin by forming a gradient of partiallycured resin between the building surface of the object and apolymerization-inhibited “dead zone.” In some embodiments, asemi-permeable membrane is used to control transport of aphotopolymerization inhibitor (e.g., oxygen) into the dead zone in orderto form the polymerization gradient. Continuous liquid interphaseprinting can achieve fabrication speeds about 25 times to about 100times faster than other direct fabrication methods, and speeds about1000 times faster can be achieved with the incorporation of coolingsystems. Continuous liquid interphase printing is described in U.S.Patent Publication Nos. 2015/0097315, 2015/0097316, and 2015/0102532,the disclosures of each of which are incorporated herein by reference intheir entirety.

As another example, a continuous direct fabrication method can achievecontinuous build-up of an object geometry by continuous movement of thebuild platform (e.g., along the vertical or Z-direction) during theirradiation phase, such that the hardening depth of the irradiatedphotopolymer is controlled by the movement speed. Accordingly,continuous polymerization of material on the build surface can beachieved. Such methods are described in U.S. Pat. No. 7,892,474, thedisclosure of which is incorporated herein by reference in its entirety.

In another example, a continuous direct fabrication method can involveextruding a composite material composed of a curable liquid materialsurrounding a solid strand. The composite material can be extruded alonga continuous three-dimensional path in order to form the object. Suchmethods are described in U.S. Patent Publication No. 2014/0061974, thedisclosure of which is incorporated herein by reference in its entirety.

In yet another example, a continuous direct fabrication method utilizesa “heliolithography” approach in which the liquid photopolymer is curedwith focused radiation while the build platform is continuously rotatedand raised. Accordingly, the object geometry can be continuously builtup along a spiral build path. Such methods are described in U.S. PatentPublication No. 2014/0265034, the disclosure of which is incorporatedherein by reference in its entirety.

The direct fabrication approaches provided herein are compatible with awide variety of materials, including but not limited to one or more ofthe following: a polyester, a co-polyester, a polycarbonate, athermoplastic polyurethane, a polypropylene, a polyethylene, apolypropylene and polyethylene copolymer, an acrylic, a cyclic blockcopolymer, a polyetheretherketone, a polyamide, a polyethyleneterephthalate, a polybutylene terephthalate, a polyetherimide, apolyethersulfone, a polytrimethylene terephthalate, a styrenic blockcopolymer (SBC), a silicone rubber, an elastomeric alloy, athermoplastic elastomer (TPE), a thermoplastic vulcanizate (TPV)elastomer, a polyurethane elastomer, a block copolymer elastomer, apolyolefin blend elastomer, a thermoplastic co-polyester elastomer, athermoplastic polyamide elastomer, a thermoset material, or combinationsthereof. The materials used for direct fabrication can be provided in anuncured form (e.g., as a liquid, resin, powder, etc.) and can be cured(e.g., by photopolymerization, light curing, gas curing, laser curing,crosslinking, etc.) in order to form an orthodontic appliance or aportion thereof. The properties of the material before curing may differfrom the properties of the material after curing. Once cured, thematerials herein can exhibit sufficient strength, stiffness, durability,biocompatibility, etc. for use in an orthodontic appliance. Thepost-curing properties of the materials used can be selected accordingto the desired properties for the corresponding portions of theappliance.

In some embodiments, relatively rigid portions of the orthodonticappliance can be formed via direct fabrication using one or more of thefollowing materials: a polyester, a co-polyester, a polycarbonate, athermoplastic polyurethane, a polypropylene, a polyethylene, apolypropylene and polyethylene copolymer, an acrylic, a cyclic blockcopolymer, a polyetheretherketone, a polyamide, a polyethyleneterephthalate, a polybutylene terephthalate, a polyetherimide, apolyethersulfone, and/or a polytrimethylene terephthalate.

In some embodiments, relatively elastic portions of the orthodonticappliance can be formed via direct fabrication using one or more of thefollowing materials: a styrenic block copolymer (SBC), a siliconerubber, an elastomeric alloy, a thermoplastic elastomer (TPE), athermoplastic vulcanizate (TPV) elastomer, a polyurethane elastomer, ablock copolymer elastomer, a polyolefin blend elastomer, a thermoplasticco-polyester elastomer, and/or a thermoplastic polyamide elastomer.

Machine parameters can include curing parameters. For digital lightprocessing (DLP)-based curing systems, curing parameters can includepower, curing time, and/or grayscale of the full image. For laser-basedcuring systems, curing parameters can include power, speed, beam size,beam shape and/or power distribution of the beam. For printing systems,curing parameters can include material drop size, viscosity, and/orcuring power. These machine parameters can be monitored and adjusted ona regular basis (e.g., some parameters at every 1-x layers and someparameters after each build) as part of the process control on thefabrication machine. Process control can be achieved by including asensor on the machine that measures power and other beam parametersevery layer or every few seconds and automatically adjusts them with afeedback loop. For DLP machines, gray scale can be measured andcalibrated before, during, and/or at the end of each build, and/or atpredetermined time intervals (e.g., every nth build, once per hour, onceper day, once per week, etc.), depending on the stability of the system.In addition, material properties and/or photo-characteristics can beprovided to the fabrication machine, and a machine process controlmodule can use these parameters to adjust machine parameters (e.g.,power, time, gray scale, etc.) to compensate for variability in materialproperties. By implementing process controls for the fabricationmachine, reduced variability in appliance accuracy and residual stresscan be achieved.

Optionally, the direct fabrication methods described herein allow forfabrication of an appliance including multiple materials, referred toherein as “multi-material direct fabrication.” In some embodiments, amulti-material direct fabrication method involves concurrently formingan object from multiple materials in a single manufacturing step. Forinstance, a multi-tip extrusion apparatus can be used to selectivelydispense multiple types of materials from distinct material supplysources in order to fabricate an object from a plurality of differentmaterials. Such methods are described in U.S. Pat. No. 6,749,414, thedisclosure of which is incorporated herein by reference in its entirety.Alternatively or in combination, a multi-material direct fabricationmethod can involve forming an object from multiple materials in aplurality of sequential manufacturing steps. For instance, a firstportion of the object can be formed from a first material in accordancewith any of the direct fabrication methods herein, and then a secondportion of the object can be formed from a second material in accordancewith methods herein, and so on, until the entirety of the object hasbeen formed.

Direct fabrication can provide various advantages compared to othermanufacturing approaches. For instance, in contrast to indirectfabrication, direct fabrication permits production of an orthodonticappliance without utilizing any molds or templates for shaping theappliance, thus reducing the number of manufacturing steps involved andimproving the resolution and accuracy of the final appliance geometry.Additionally, direct fabrication permits precise control over thethree-dimensional geometry of the appliance, such as the appliancethickness. Complex structures and/or auxiliary components can be formedintegrally as a single piece with the appliance shell in a singlemanufacturing step, rather than being added to the shell in a separatemanufacturing step. In some embodiments, direct fabrication is used toproduce appliance geometries that would be difficult to create usingalternative manufacturing techniques, such as appliances with very smallor fine features, complex geometric shapes, undercuts, interproximalstructures, shells with variable thicknesses, and/or internal structures(e.g., for improving strength with reduced weight and material usage).For example, in some embodiments, the direct fabrication approachesherein permit fabrication of an orthodontic appliance with feature sizesof less than or equal to about 5 μm, or within a range from about 5 μmto about 50 μm, or within a range from about 20 μm to about 50 μm.

The direct fabrication techniques described herein can be used toproduce appliances with substantially isotropic material properties,e.g., substantially the same or similar strengths along all directions.In some embodiments, the direct fabrication approaches herein permitproduction of an orthodontic appliance with a strength that varies by nomore than about 25%, about 20%, about 15%, about 10%, about 5%, about1%, or about 0.5% along all directions. Additionally, the directfabrication approaches herein can be used to produce orthodonticappliances at a faster speed compared to other manufacturing techniques.In some embodiments, the direct fabrication approaches herein allow forproduction of an orthodontic appliance in a time interval less than orequal to about 1 hour, about 30 minutes, about 25 minutes, about 20minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 4minutes, about 3 minutes, about 2 minutes, about 1 minutes, or about 30seconds. Such manufacturing speeds allow for rapid “chair-side”production of customized appliances, e.g., during a routine appointmentor checkup.

In some embodiments, the direct fabrication methods described hereinimplement process controls for various machine parameters of a directfabrication system or device in order to ensure that the resultantappliances are fabricated with a high degree of precision. Suchprecision can be beneficial for ensuring accurate delivery of a desiredforce system to the teeth in order to effectively elicit toothmovements. Process controls can be implemented to account for processvariability arising from multiple sources, such as the materialproperties, machine parameters, environmental variables, and/orpost-processing parameters.

Material properties may vary depending on the properties of rawmaterials, purity of raw materials, and/or process variables duringmixing of the raw materials. In many embodiments, resins or othermaterials for direct fabrication should be manufactured with tightprocess control to ensure little variability in photo-characteristics,material properties (e.g., viscosity, surface tension), physicalproperties (e.g., modulus, strength, elongation) and/or thermalproperties (e.g., glass transition temperature, heat deflectiontemperature). Process control for a material manufacturing process canbe achieved with screening of raw materials for physical propertiesand/or control of temperature, humidity, and/or other process parametersduring the mixing process. By implementing process controls for thematerial manufacturing procedure, reduced variability of processparameters and more uniform material properties for each batch ofmaterial can be achieved. Residual variability in material propertiescan be compensated with process control on the machine, as discussedfurther herein.

Machine parameters can include curing parameters. For digital lightprocessing (DLP)-based curing systems, curing parameters can includepower, curing time, and/or grayscale of the full image. For laser-basedcuring systems, curing parameters can include power, speed, beam size,beam shape and/or power distribution of the beam. For printing systems,curing parameters can include material drop size, viscosity, and/orcuring power. These machine parameters can be monitored and adjusted ona regular basis (e.g., some parameters at every 1-x layers and someparameters after each build) as part of the process control on thefabrication machine. Process control can be achieved by including asensor on the machine that measures power and other beam parametersevery layer or every few seconds and automatically adjusts them with afeedback loop. For DLP machines, gray scale can be measured andcalibrated at the end of each build. In addition, material propertiesand/or photo-characteristics can be provided to the fabrication machine,and a machine process control module can use these parameters to adjustmachine parameters (e.g., power, time, gray scale, etc.) to compensatefor variability in material properties. By implementing process controlsfor the fabrication machine, reduced variability in appliance accuracyand residual stress can be achieved.

In many embodiments, environmental variables (e.g., temperature,humidity, Sunlight or exposure to other energy/curing source) aremaintained in a tight range to reduce variable in appliance thicknessand/or other properties. Optionally, machine parameters can be adjustedto compensate for environmental variables.

In many embodiments, post-processing of appliances includes cleaning,post-curing, and/or support removal processes. Relevant post-processingparameters can include purity of cleaning agent, cleaning pressureand/or temperature, cleaning time, post-curing energy and/or time,and/or consistency of support removal process. These parameters can bemeasured and adjusted as part of a process control scheme. In addition,appliance physical properties can be varied by modifying thepost-processing parameters. Adjusting post-processing machine parameterscan provide another way to compensate for variability in materialproperties and/or machine properties.

Once appliances (e.g., aligners) are directly fabricated, they may beinspected using the systems and/or methods described herein above.

The configuration of the orthodontic appliances herein can be determinedaccording to a treatment plan for a patient, e.g., a treatment planinvolving successive administration of a plurality of appliances forincrementally repositioning teeth. Computer-based treatment planningand/or appliance manufacturing methods can be used in order tofacilitate the design and fabrication of appliances. For instance, oneor more of the appliance components described herein can be digitallydesigned and fabricated with the aid of computer-controlledmanufacturing devices (e.g., computer numerical control (CNC) milling,computer-controlled rapid prototyping such as 3D printing, etc.). Thecomputer-based methods presented herein can improve the accuracy,flexibility, and convenience of appliance fabrication.

FIG. 13 illustrates a method 1300 of orthodontic treatment using aplurality of appliances, in accordance with embodiments. The method 1300can be practiced using any of the appliances or appliance sets describedherein. In block 1302, a first orthodontic appliance is applied to apatient's teeth in order to reposition the teeth from a first tootharrangement to a second tooth arrangement. In block 1304, a secondorthodontic appliance is applied to the patient's teeth in order toreposition the teeth from the second tooth arrangement to a third tootharrangement. The method 1300 can be repeated as necessary using anysuitable number and combination of sequential appliances in order toincrementally reposition the patient's teeth from an initial arrangementto a target arrangement. The appliances can be generated all at the samestage or in sets or batches (e.g., at the beginning of a stage of thetreatment), or the appliances can be fabricated one at a time, and thepatient can wear each appliance until the pressure of each appliance onthe teeth can no longer be felt or until the maximum amount of expressedtooth movement for that given stage has been achieved. A plurality ofdifferent appliances (e.g., a set) can be designed and even fabricatedprior to the patient wearing any appliance of the plurality. Afterwearing an appliance for an appropriate period of time, the patient canreplace the current appliance with the next appliance in the seriesuntil no more appliances remain. The appliances are generally notaffixed to the teeth and the patient may place and replace theappliances at any time during the procedure (e.g., patient-removableappliances). The final appliance or several appliances in the series mayhave a geometry or geometries selected to overcorrect the tootharrangement. For instance, one or more appliances may have a geometrythat would (if fully achieved) move individual teeth beyond the tootharrangement that has been selected as the “final.” Such over-correctionmay be desirable in order to offset potential relapse after therepositioning method has been terminated (e.g., permit movement ofindividual teeth back toward their pre-corrected positions).Over-correction may also be beneficial to speed the rate of correction(e.g., an appliance with a geometry that is positioned beyond a desiredintermediate or final position may shift the individual teeth toward theposition at a greater rate). In such cases, the use of an appliance canbe terminated before the teeth reach the positions defined by theappliance. Furthermore, over-correction may be deliberately applied inorder to compensate for any inaccuracies or limitations of theappliance.

FIG. 14 illustrates a method 1400 for designing an orthodontic applianceto be produced by direct fabrication, in accordance with embodiments.The method 1400 can be applied to any embodiment of the orthodonticappliances described herein. Some or all of the blocks of the method1400 can be performed by any suitable data processing system or device,e.g., one or more processors configured with suitable instructions.

In block 1402, a movement path to move one or more teeth from an initialarrangement to a target arrangement is determined. The initialarrangement can be determined from a mold or a scan of the patient'steeth or mouth tissue, e.g., using wax bites, direct contact scanning,x-ray imaging, tomographic imaging, sonographic imaging, and othertechniques for obtaining information about the position and structure ofthe teeth, jaws, gums and other orthodontically relevant tissue. Fromthe obtained data, a digital data set can be derived that represents theinitial (e.g., pretreatment) arrangement of the patient's teeth andother tissues. Optionally, the initial digital data set is processed tosegment the tissue constituents from each other. For example, datastructures that digitally represent individual tooth crowns can beproduced. Advantageously, digital models of entire teeth can beproduced, including measured or extrapolated hidden surfaces and rootstructures, as well as surrounding bone and soft tissue.

The target arrangement of the teeth (e.g., a desired and intended endresult of orthodontic treatment) can be received from a clinician in theform of a prescription, can be calculated from basic orthodonticprinciples, and/or can be extrapolated computationally from a clinicalprescription. With a specification of the desired final positions of theteeth and a digital representation of the teeth themselves, the finalposition and surface geometry of each tooth can be specified to form acomplete model of the tooth arrangement at the desired end of treatment.

Having both an initial position and a target position for each tooth, amovement path can be defined for the motion of each tooth. In someembodiments, the movement paths are configured to move the teeth in thequickest fashion with the least amount of round-tripping to bring theteeth from their initial positions to their desired target positions.The tooth paths can optionally be segmented, and the segments can becalculated so that each tooth's motion within a segment stays withinthreshold limits of linear and rotational translation. In this way, theend points of each path segment can constitute a clinically viablerepositioning, and the aggregate of segment end points can constitute aclinically viable sequence of tooth positions, so that moving from onepoint to the next in the sequence does not result in a collision ofteeth.

In block 1404, a force system to produce movement of the one or moreteeth along the movement path is determined. A force system can includeone or more forces and/or one or more torques. Different force systemscan result in different types of tooth movement, such as tipping,translation, rotation, extrusion, intrusion, root movement, etc.Biomechanical principles, modeling techniques, forcecalculation/measurement techniques, and the like, including knowledgeand approaches commonly used in orthodontia, may be used to determinethe appropriate force system to be applied to the tooth to accomplishthe tooth movement. In determining the force system to be applied,sources may be considered including literature, force systems determinedby experimentation or virtual modeling, computer-based modeling,clinical experience, minimization of unwanted forces, etc.

The determination of the force system can include constraints on theallowable forces, such as allowable directions and magnitudes, as wellas desired motions to be brought about by the applied forces. Forexample, in fabricating palatal expanders, different movement strategiesmay be desired for different patients. For example, the amount of forceneeded to separate the palate can depend on the age of the patient, asvery young patients may not have a fully-formed suture. Thus, injuvenile patients and others without fully-closed palatal sutures,palatal expansion can be accomplished with lower force magnitudes.Slower palatal movement can also aid in growing bone to fill theexpanding suture. For other patients, a more rapid expansion may bedesired, which can be achieved by applying larger forces. Theserequirements can be incorporated as needed to choose the structure andmaterials of appliances; for example, by choosing palatal expanderscapable of applying large forces for rupturing the palatal suture and/orcausing rapid expansion of the palate. Subsequent appliance stages canbe designed to apply different amounts of force, such as first applyinga large force to break the suture, and then applying smaller forces tokeep the suture separated or gradually expand the palate and/or arch.

The determination of the force system can also include modeling of thefacial structure of the patient, such as the skeletal structure of thejaw and palate. Scan data of the palate and arch, such as X-ray data or3D optical scanning data, for example, can be used to determineparameters of the skeletal and muscular system of the patient's mouth,so as to determine forces sufficient to provide a desired expansion ofthe palate and/or arch. In some embodiments, the thickness and/ordensity of the mid-palatal suture may be measured, or input by atreating professional. In other embodiments, the treating professionalcan select an appropriate treatment based on physiologicalcharacteristics of the patient. For example, the properties of thepalate may also be estimated based on factors such as the patient'sage—for example, young juvenile patients will typically require lowerforces to expand the suture than older patients, as the suture has notyet fully formed.

In block 1406, an orthodontic appliance configured to produce the forcesystem is determined. Determination of the orthodontic appliance,appliance geometry, material composition, and/or properties can beperformed using a treatment or force application simulation environment.A simulation environment can include, e.g., computer modeling systems,biomechanical systems or apparatus, and the like. Optionally, digitalmodels of the appliance and/or teeth can be produced, such as finiteelement models. The finite element models can be created using computerprogram application software available from a variety of vendors. Forcreating solid geometry models, computer aided engineering (CAE) orcomputer aided design (CAD) programs can be used, such as the AutoCAD®software products available from Autodesk, Inc., of San Rafael, Calif.For creating finite element models and analyzing them, program productsfrom a number of vendors can be used, including finite element analysispackages from ANSYS, Inc., of Canonsburg, Pa., and SIMULIA(Abaqus)software products from Dassault Systémes of Waltham, Mass.

Optionally, one or more orthodontic appliances can be selected fortesting or force modeling. As noted above, a desired tooth movement, aswell as a force system required or desired for eliciting the desiredtooth movement, can be identified. Using the simulation environment, acandidate orthodontic appliance can be analyzed or modeled fordetermination of an actual force system resulting from use of thecandidate appliance. One or more modifications can optionally be made toa candidate appliance, and force modeling can be further analyzed asdescribed, e.g., in order to iteratively determine an appliance designthat produces the desired force system.

In block 1408, instructions for fabrication of the orthodontic applianceincorporating the orthodontic appliance are generated. The instructionscan be configured to control a fabrication system or device in order toproduce the orthodontic appliance with the specified orthodonticappliance. In some embodiments, the instructions are configured formanufacturing the orthodontic appliance using direct fabrication (e.g.,stereolithography, selective laser sintering, fused deposition modeling,3D printing, continuous direct fabrication, multi-material directfabrication, etc.), in accordance with the various methods presentedherein. In alternative embodiments, the instructions can be configuredfor indirect fabrication of the appliance, e.g., by thermoforming.

Method 1400 may comprise additional blocks: 1) The upper arch and palateof the patient is scanned intraorally to generate three dimensional dataof the palate and upper arch; 2) The three dimensional shape profile ofthe appliance is determined to provide a gap and teeth engagementstructures as described herein.

Although the above blocks show a method 1400 of designing an orthodonticappliance in accordance with some embodiments, a person of ordinaryskill in the art will recognize some variations based on the teachingdescribed herein. Some of the blocks may comprise sub-blocks. Some ofthe blocks may be repeated as often as desired. One or more blocks ofthe method 1400 may be performed with any suitable fabrication system ordevice, such as the embodiments described herein. Some of the blocks maybe optional, and the order of the blocks can be varied as desired.

FIG. 15 illustrates a method 1500 for digitally planning an orthodontictreatment and/or design or fabrication of an appliance, in accordancewith embodiments. The method 1500 can be applied to any of the treatmentprocedures described herein and can be performed by any suitable dataprocessing system.

In block 1510, a digital representation of a patient's teeth isreceived. The digital representation can include surface topography datafor the patient's intraoral cavity (including teeth, gingival tissues,etc.). The surface topography data can be generated by directly scanningthe intraoral cavity, a physical model (positive or negative) of theintraoral cavity, or an impression of the intraoral cavity, using asuitable scanning device (e.g., a handheld scanner, desktop scanner,etc.).

In block 1502, one or more treatment stages are generated based on thedigital representation of the teeth. The treatment stages can beincremental repositioning stages of an orthodontic treatment proceduredesigned to move one or more of the patient's teeth from an initialtooth arrangement to a target arrangement. For example, the treatmentstages can be generated by determining the initial tooth arrangementindicated by the digital representation, determining a target tootharrangement, and determining movement paths of one or more teeth in theinitial arrangement necessary to achieve the target tooth arrangement.The movement path can be optimized based on minimizing the totaldistance moved, preventing collisions between teeth, avoiding toothmovements that are more difficult to achieve, or any other suitablecriteria.

In block 1504, at least one orthodontic appliance is fabricated based onthe generated treatment stages. For example, a set of appliances can befabricated, each shaped according a tooth arrangement specified by oneof the treatment stages, such that the appliances can be sequentiallyworn by the patient to incrementally reposition the teeth from theinitial arrangement to the target arrangement. The appliance set mayinclude one or more of the orthodontic appliances described herein. Thefabrication of the appliance may involve creating a digital model of theappliance to be used as input to a computer-controlled fabricationsystem. The appliance can be formed using direct fabrication methods,indirect fabrication methods, or combinations thereof, as desired.

In some instances, staging of various arrangements or treatment stagesmay not be necessary for design and/or fabrication of an appliance.Design and/or fabrication of an orthodontic appliance, and perhaps aparticular orthodontic treatment, may include use of a representation ofthe patient's teeth (e.g., receive a digital representation of thepatient's teeth), followed by design and/or fabrication of anorthodontic appliance based on a representation of the patient's teethin the arrangement represented by the received representation.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some embodiments of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular implementations may vary from these exemplarydetails and still be contemplated to be within the scope of the presentdisclosure.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” When the term “about” or “approximately” is usedherein, this is intended to mean that the nominal value presented isprecise within ±10%.

Although the operations of the methods herein are shown and described ina particular order, the order of operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittentand/or alternating manner. In one embodiment, multiple metal bondingoperations are performed as a single step.

It is understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method of performing automated quality controlfor a three-dimensional (3D) printed object, comprising: providing afirst illumination of the 3D printed object using a first light sourcearrangement; generating a plurality of images of the 3D printed objectusing one or more imaging devices, wherein each image of the pluralityof images depicts a distinct region of the 3D printed object; processingthe plurality of images by a processing device using a machine learningmodel trained to identify one or more types of manufacturing defects ofa 3D printing process, wherein an output of the machine learning modelcomprises, for each type of manufacturing defect, a probability that animage comprises a defect of that type of manufacturing defect; anddetermining, by the processing device and without user input, whetherthe 3D printed object comprises one or more manufacturing defects basedon a result of the processing.
 2. The method of claim 1, wherein the oneor more types of manufacturing defects comprise at least one of aninternal volume defect within an internal volume of the 3D printedobject, a surface defect on a surface of the 3D printed object, or aninterface defect at an interface of the internal volume and the surface.3. The method of claim 1, further comprising performing the followingfor each image of the plurality of images: determining whether the imagecomprises a contrast that renders the image unprocessable by the machinelearning model; and responsive to determining that the image isprocessable by the machine learning model, processing the image by theprocessing device to determine whether a defect is present in a regionof the 3D printed object depicted in the image.
 4. The method of claim3, further comprising, responsive to determining that the image isunprocessable by the machine learning model, performing the following:providing a second illumination of the 3D printed object using a secondlight source arrangement, wherein the second light source arrangementgenerates a different shadow pattern on the 3D printed object than thefirst light source arrangement; and generating a new version of theimage while the 3D printed object is illuminated by the second lightsource arrangement.
 5. The method of claim 1, wherein the 3D printedobject is associated with an identifier (ID) that is printed on the 3Dprinted object, the method further comprising: providing an initialillumination that is different from the first illumination; generating afirst image while the initial illumination is provided; performingoptical character recognition on the first image; and determining the IDassociated with the 3D printed object based on a result of the opticalcharacter recognition.
 6. The method of claim 1, further comprising:determining a digital file associated with the 3D printed object;determining, from the digital file associated with the 3D printedobject, a geometry associated with at least one surface of the 3Dprinted object; and selecting the first light source arrangement toprovide the first illumination based on the at least one surface.
 7. Themethod of claim 1, further comprising: determining a digital fileassociated with the 3D printed object; determining a first silhouetteassociated with the 3D printed object from the digital file; determininga second silhouette of the 3D printed object from at least one image ofthe plurality of images; comparing the first silhouette to the secondsilhouette; determining a difference metric between the first silhouetteand the second silhouette based on the comparing; determining whetherthe difference metric exceeds a difference threshold; and determiningthat the 3D printed object comprises a gross defect responsive todetermining that the difference metric exceeds the difference threshold.8. The method of claim 1, wherein the 3D printed object comprises atleast one of a mold of a dental arch of a patient at a treatment stagein an orthodontic treatment, or a polymeric orthodontic aligner for thepatient at the treatment stage in the orthodontic treatment.
 9. Themethod of claim 1, further comprising performing the following for eachimage of the first plurality of images: performing edge detection on theimage to determine a boundary of the 3D printed object in the image;selecting a set of points on the boundary; determining an area ofinterest using the set of points, wherein the area of interest comprisesa first region of the image that depicts the 3D printed object withinthe boundary; and cropping the image to exclude a second region of theimage that is outside of the area of interest, wherein the cropped imageis processed by the processing device using the machine learning model.10. The method of claim 1, further comprising: determining that the 3Dprinted object comprises one or more manufacturing defects; determininga 3D printer that manufactured the 3D printed object; determining thatthe 3D printer has manufactured one or more additional 3D printedobjects that also included manufacturing defects; and schedulingmaintenance of the 3D printer.
 11. The method of claim 1, wherein anoutput of the machine learning model further comprises an indication ofa severity of a detected manufacturing defect, and wherein determiningwhether the 3D printed object comprises one or more manufacturingdefects comprises determining, based at least in part of the severity ofthe detected manufacturing defect, whether the 3D printed objectcomprises one or more manufacturing defects that alone or together willdegrade a performance of the 3D printed object.
 12. A method comprising,obtaining, by a processing device, an image of a three-dimensional (3D)printed object; performing edge detection on the image to determine aboundary of the 3D printed object in the image; selecting a set ofpoints on the boundary; determining an area of interest using the set ofpoints, wherein the area of interest comprises a first region of theimage that depicts the 3D printed object within the boundary; croppingthe image to exclude a second region of the image that is outside of thearea of interest; processing the cropped image using a machine learningmodel trained to identify manufacturing defects of a 3D printingprocess, wherein an output of the machine learning model comprises aprobability that the 3D printed object in the image comprises amanufacturing defect; and determining whether the 3D printed objectdepicted in the image comprises a defect from the output.
 13. The methodof claim 12, wherein determining the region of interest comprises:fitting the set of points on the boundary to one or more geometricshapes; determining a geometric shape of the one or more geometricshapes that most closely fits the set of points; and defining an areawithin the geometric shape as the area of interest.
 14. The method ofclaim 12, wherein the output further comprises an identification of alocation within the image where a defect was identified, the methodfurther comprising: highlighting the location of the defect in theimage.
 15. The method of claim 12, wherein the machine learning model istrained to identify one or more types of manufacturing defects, whereinthe one or more types of manufacturing defects comprise at least one ofan internal volume defect within an internal volume of the 3D printedobject, a surface defect on a surface of the 3D printed object, or aninterface defect at an interface of the internal volume and the surface.16. The method of claim 12, wherein the 3D printed object comprises atleast one of a mold of a dental arch of a patient at a treatment stagein an orthodontic treatment, or a polymeric orthodontic aligner for thepatient at the treatment stage in the orthodontic treatment.
 17. Adefect detection system of three-dimensional (3D) printed objects,comprising: a multi-axis platform to support a 3D printed object; aplurality of light sources to illuminate the 3D printed object; one ormore imaging devices to generate a plurality of images of the 3D printedobject from a plurality of rotation or translational motion settings ofthe multi-axis platform, wherein each image of the plurality of imagesdepicts a distinct region of the 3D printed object; and a computingdevice to: process the first plurality of images using a machinelearning model trained to identify manufacturing defects of a 3Dprinting process, wherein an output of the machine learning modelcomprises a probability that an image comprises a manufacturing defect;and determine, without user input, whether the 3D printed objectcomprises one or more manufacturing defects based on a result of theoutput of the machine learning model.
 18. The defect detection system ofclaim 17, wherein the machine learning model is trained to identify oneor more types of manufacturing defects, wherein the one or more types ofmanufacturing defects comprise at least one of an internal volume defectwithin an internal volume of the 3D printed object, a surface defect ona surface of the 3D printed object, or an interface defect at aninterface of the internal volume and the surface.
 19. The defectdetection system of claim 17, wherein the computing device is further toperform the following for each image of the plurality of images:determine whether the image comprises a contrast that renders the imageunprocessable by the machine learning model; and responsive todetermining that the image is unprocessable by the machine learningmodel, perform the following comprising: cause the plurality of lightsources to modify an illumination of the 3D printed object; cause themulti-axis platform to rotate to a rotation or translational motionsetting associated with the image; and cause an imaging device of theone or more imaging devices to generate a new image at the rotation ortranslational motion setting.
 20. The defect detection system of claim17, wherein the computing device is further to perform the following foreach image of the plurality of images: perform edge detection on theimage to determine a boundary of the 3D printed object in the image;select a set of points on the boundary; determine an area of interestusing the set of points, wherein the area of interest comprises a firstregion of the image that depicts the 3D printed object within theboundary; and crop the image to exclude a second region of the imagethat is outside of the area of interest, wherein the cropped image isprocessed by the computing device using the machine learning model. 21.The defect detection system of claim 17, wherein the computing device isfurther to: determine a digital file associated with the 3D printedobject; determine a first silhouette associated with the 3D printedobject from the digital file; determine a second silhouette of the 3Dprinted object from at least one image of the first plurality of images;make a comparison between the first silhouette and the secondsilhouette; determine a difference metric between the first silhouetteand the second silhouette based on the comparison; determine whether thedifference metric exceeds a difference threshold; and determine that the3D printed object comprises a gross defect responsive to determiningthat the difference metric exceeds the difference threshold.